13,251 AI Researcher jobs in India

**AI Researcher (Language, Voice and Image)**

**Reports to : AI Director, Technology**

**About Digital Green**

Digital Green is a pioneer global not for profit organization, utilizing digital platforms and community-driven approaches to amplify the voices of smallholder farmers and improve their livelihoods. Our mission is to create a world where farmers use technology and data to build prosperous communities. By harnessing the power of technology, we facilitate knowledge sharing, capacity building, and market linkages, enabling farmers to adopt sustainable agricultural practices and increase their productivity and income.

We are dedicated to transforming the lives of under-served smallholder farmers worldwide through innovative technology solutions. Backed by leading philanthropic organizations such as the Bill & Melinda Gates Foundation (BMGF), Walmart Foundation, USAID, and UK Foreign, Commonwealth & Development Office (UKFCDO), we are committed to leveraging data and technology to empower smallholder farmers and strengthen agricultural extension systems.

**Job Summary**

As an AI Researcher at Digital Green, you will be at the forefront of innovation, driving the development of language, voice, and computer vision models specifically tailored for the agricultural domain. Your work will directly contribute to enhancing the capabilities of our conversational bots, voice-enabled interfaces, and computer vision systems, enabling them to effectively understand and respond to user queries in local languages, recognize speech accurately, and analyze agricultural images. This role focuses on leveraging cutting-edge machine learning techniques to address key challenges faced by smallholder farmers, ultimately driving positive impact and transformation in agricultural practices and livelihoods.

Key Responsibilities:

- Domain-specific Language Modeling:

- Develop domain-specific language models trained on agricultural text data to improve our bots' ability to understand and generate content related to farming practices, crop management, pest control, weather forecasting, and other relevant topics. This includes enhancing comprehension and generation in local languages commonly used by farmers and designing algorithms to recognize and interpret agricultural terminology, abbreviations, and regional dialects.
- Voice Recognition Model Development:

- Develop and optimize automatic speech recognition (ASR) models for native languages spoken by smallholder farmers, incorporating domain-specific terminologies and regional dialects, using advanced techniques like deep learning, transfer learning, and data augmentation to ensure high accuracy and reliability in real-world settings.
- Computer Vision Model Development:

- Data Collection and Annotation:

- Formulate and coordinate a comprehensive plan with program teams and third-party services to collect, annotate, and ensure high-quality agricultural text, speech, and image data in local languages, capturing diverse linguistic variations, agricultural contexts, and image scenarios for training and fine-tuning models.
- Model Training, Optimization, and Evaluation:

- Train and optimize language, ASR, and computer vision models using state-of-the-art techniques to achieve high accuracy and reliability.
- Evaluate model performance using key metrics such as accuracy, word error rate, precision, recall, and F1 score, and iteratively refine models based on feedback and insights.
- Collaboration and Integration:

- Collaborate closely with software engineers, data scientists, agricultural experts, and product managers to integrate AI capabilities into Digital Green's platforms, ensuring seamless functionality and user experience.
- Share insights and learnings with internal teams, government partners, and stakeholders to facilitate knowledge transfer and capacity building.
- Continuous Improvement and Knowledge Sharing:

- Stay updated with the latest advancements in NLP, ASR, and computer vision research and techniques, continuously refining and optimizing models to deliver state-of-the-art performance.
- Document model architectures, training procedures, and best practices, and share knowledge with cross-functional teams and stakeholders.

Qualifications:

- Education: Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Engineering, or a related field with a focus on NLP, speech recognition, or computer vision.
- Experience: Proven experience of 8+ years in AI research, with a strong track record of developing language models, ASR models, and computer vision algorithms. Experience in the agricultural domain is a plus.
- Programming Skills: Proficiency in Python and experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face Transformers, Kaldi, Mozilla DeepSpeech, OpenCV, and Keras.
- Analytical Skills: Strong analytical and problem-solving skills, with the ability to critically evaluate research papers, des

**About Us**

Aquaticode builds artificial intelligence solutions for aquaculture. Our core competency

lies at the intersection of biology and artificial intelligence, utilizing specialized imaging

technology to detect, identify, and predict traits of aquatic species. We value

commitment and creativity in building real-world solutions that benefit humanity.

**Position Overview**

We are seeking a talented and ambitious AI Researcher with expertise in computer

vision and strong development skills to join our team. You will play a key role in

designing and implementing cutting-edge machine learning algorithms to solve

challenging problems in aquaculture.

**Responsibilities**
- Design, adapt, and implement machine learning and classical algorithms from

proof of concept (POC) to working prototypes.
- Plan and conduct experiments to address critical business questions.
- Develop and maintain model testing and statistical verification processes.
- Implement data processing and training pipelines.
- Extend existing machine learning and deep learning codebases and frameworks.
- Thoroughly document POCs and experiments.
- Plan and lead long-term research activities.
- Assist in recruiting talent in the field.
- Stay up-to-date with the latest developments in AI and machine learning.

**Requirements**:

- Proven track record in developing image/video processing and computer vision

solutions.
- 3+ years of experience as a machine learning engineer/researcher or in a similar

role.
- Proficiency with common Python machine learning frameworks (scikit-learn,

SciPy, Matplotlib, PyTorch, etc.).
- Strong understanding of data structures, data modeling, and software

architecture.
- Ability to write clean, robust, and efficient code.
- Excellent communication and presentation skills in English.
- MSc or PhD in Computer Science, Engineering, Mathematics, or a related field.

encouraged to apply.

**Preferred Qualifications**
- A track record of publications in top-tier AI conferences or journals.
- Experience deploying machine learning/AI systems in production environments.
- Familiarity with containerization technologies (e.g., Docker).
- Knowledge of databases (SQL, MongoDB) and basic networking or message
- passing protocols.
- Experience with cloud platforms (Google Cloud Platform, AWS, Azure).
- Proficiency with MLOps frameworks (DVC, MLflow, Metaflow, Databricks).
- Familiarity with CI/CD tools (Jenkins, GitLab CI/CD, Screwdriver, Spinnaker, or

similar).

**About Nacre Capital**

We were founded by Nacre Capital, a venture builder focused on AI within the life

sciences. Nacre has an impressive track record in creating, building, and growing deep

Job Location: Chennai, Bangalore, Noida, Hyderabad

As a Gen AI Researcher, you will develop and optimize domain specific large language models (LLMs) and small language models (SLMs) to address unique domain specific challenges and deliver innovative solutions. Your role includes creating advanced SLMs and LLMs for specific domains, focusing on supervised fine tuning, reinforcement learning from human feedback (RLHF), pre and post training, evaluation, and multi-modalities.

Key Responsibilities:

• Conduct cutting edge research in the field of NLP and develop advanced SLMs and LLMs tailored to domain specific applications.
• Design, implement, and optimize language models to solve domain specific problems effectively and efficiently.
• Analyze and preprocess large datasets to ensure high-quality input for training SLMs and LLMs.
• Design and Evaluate the performance of language models using appropriate metrics and benchmarks, and iterate on improvements.
• Developing and implementing methods that extend and improve model capabilities, reliability, and safety
• Work closely with cross functional teams, including domain experts, data engineers, and software engineers, to integrate language models into the production.
• Publish research findings in top tier conferences and journals, and communicate results to both technical and non technical stakeholders.
• Keep abreast of the latest advancements in the field of NLP, ML, and AI, and apply relevant techniques to ongoing projects.

Qualifications:

• Ph.D. in Computer Science, Computational Linguistics , Data Science, Artificial Intelligence, or a related field with a focus on NLP or ML.
• Bachelor's or Master’s degree in Computer Science, Computer Engineering, relevant technical field.
• At least 1 year of experience in developing and optimizing SLMs and LLMs, focusing on foundation model training, supervised fine tuning, reinforcement learning from human feedback (RLHF).
• Solid understanding of various fine-tuning techniques like full fine tuning, PEFT techniques like LoRA, QLoRA and the strategy to adopt for various objectives
• Have solid ML and RL software engineering and scientific research skills
• Have produced novel research related to fine tuning SLMs / LLMs or improving upon pretrained SLMs / LLMs
• Proven experience in Gen AI libraries / frameworks, including but not limited to LangChain, LangGraph, LlamaIndex and LangSmith.
• At least 5 years of Research experience in one or more of these areas: NLP, speech, computer vision, Multimodal, machine learning, deep learning, or related fields.
• Proficiency in programming languages such as Python, and experience with NLP/ML frameworks like TensorFlow, PyTorch, and Hugging Face, as evidenced by released code (e.g. on GitHub) or elsewhere
• Familiarity with domain specific applications and the ability to translate domain knowledge into effective language models.
• Knowledge of Responsible AI practices and compliance with relevant laws is a plus.
• Industry experience with large-scale data processing, distributed computing, or cloud platforms.
• Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms is a plus.
• Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse audiences.
• Ability to work collaboratively in a team environment and contribute to a positive and productive work culture.

About the Role

QpiAI works at the intersection of AI and Quantum Computing, developing groundbreaking solutions to tackle some of the most complex challenges across various industries. Our team is dedicated to pushing the boundaries of innovation and delivering exceptional results to our enterprise clients, driving significant impact.
We are seeking an exceptional Applied AI Researcher to join our innovative research team. This role will focus on advancing our AI capabilities through rigorous experimentation, multi-agent systems design, domain-specific scaffolding, and evaluation methodology development. Through this role you will get the opportunity to develop Compound AI systems impacting areas like Drug Discovery, Decision Intelligence, Chip Design , Quantum Computing etc.

Key Responsibilities
Design and implement novel multi-agent architectures that enable complex problem-solving and collaboration
Develop domain-specific scaffolding techniques to guide AI systems in specialized environments
Create and curate high-quality datasets for training and evaluating AI systems across various domains
Establish comprehensive evaluation frameworks to measure system performance, robustness, and alignment
Research and implement reinforcement learning approaches for agent behavior optimization
Develop techniques for post-training optimization and domain adaptation
Collaborate with cross-functional teams to translate research findings into practical applications
Stay current with the latest advancements in AI research and contribute to the research community
Document methodologies, findings, and best practices for internal knowledge sharing

Qualifications
Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or related technical field
4+ years of experience in applied AI research
Strong mathematical foundations in optimization, probability theory, and linear algebra
Extensive expertise in Python programming and software development
Strong proficiency in PyTorch or JAX for implementing and scaling AI models
Experience in curating datasets, pre-training, and aligning multimodal small language models
Demonstrated experience with various reinforcement learning post-training methods (RLHF, DPO, GRPO, SFT, etc.)
Experience with prompting methods like Chain of Thought and curating Chain of Thought datasets
Experience training small reasoning models for specific applications
Familiarity with various alignment methods and value learning techniques
Experience with multi-agent systems design and implementation
Knowledge of distributed training on multi-node, multi-GPU clusters
Experience with dataset curation, cleaning, and augmentation for diverse AI tasks
Strong understanding of model evaluation metrics and methodology development
Excellent communication skills and ability to present complex technical concepts clearly

Preferred Experience
Publication record in top-tier AI/ML conferences (NeurIPS, ICLR, AAMAS, ICML)
Experience applying AI to scientific domains such as drug discovery, materials science, chip design, or mathematical reasoning
Experience working with vision-language models and multimodal architectures
Demonstrated success in developing efficient small language models for specialized tasks
Research experience in open-endedness, including emergent behaviors, auto-curriculum learning, or open-world agent systems for design of experiments, hypothesis generation.
Background in computational chemistry, physics, electrical engineering, or mathematics
Experience with Kubernetes and containerized workflow orchestration
Familiarity with ML infrastructure tools like Ray, Hydra, or MLflow
Knowledge of interpretability methods and system analysis approaches
Experience with domain adaptation for specialized scientific applications
Open-source contributions to AI research or tooling
Experience in developing custom GPU accelerated pipelines.
Experience optimizing large-scale models for inference and deployment.

What We Offer
Opportunity to work on cutting-edge AI research with real-world impact
Access to substantial computational resources for large-scale experimentation
Collaborative environment with top researchers and engineers
Flexibility to pursue novel research directions within the company's mission
Competitive compensation and benefits package

Join us in shaping the future of intelligent systems and their applications across diverse scientific domains.

Job Location: Chennai, Bangalore, Noida, Hyderabad

As a Gen AI Researcher, you will develop and optimize domain specific large language models (LLMs) and small language models (SLMs) to address unique domain specific challenges and deliver innovative solutions. Your role includes creating advanced SLMs and LLMs for specific domains, focusing on supervised fine tuning, reinforcement learning from human feedback (RLHF), pre and post training, evaluation, and multi-modalities.

Key Responsibilities:

- Conduct cutting edge research in the field of NLP and develop advanced SLMs and LLMs tailored to domain specific applications.
- Design, implement, and optimize language models to solve domain specific problems effectively and efficiently.
- Analyze and preprocess large datasets to ensure high-quality input for training SLMs and LLMs.
- Design and Evaluate the performance of language models using appropriate metrics and benchmarks, and iterate on improvements.
- Developing and implementing methods that extend and improve model capabilities, reliability, and safety
- Work closely with cross functional teams, including domain experts, data engineers, and software engineers, to integrate language models into the production.
- Publish research findings in top tier conferences and journals, and communicate results to both technical and non technical stakeholders.
- Keep abreast of the latest advancements in the field of NLP, ML, and AI, and apply relevant techniques to ongoing projects.

Qualifications:

- Ph.D. in Computer Science, Computational Linguistics , Data Science, Artificial Intelligence, or a related field with a focus on NLP or ML.
- Bachelor's or Master’s degree in Computer Science, Computer Engineering, relevant technical field.
- At least 1 year of experience in developing and optimizing SLMs and LLMs, focusing on foundation model training, supervised fine tuning, reinforcement learning from human feedback (RLHF).
- Solid understanding of various fine-tuning techniques like full fine tuning, PEFT techniques like LoRA, QLoRA and the strategy to adopt for various objectives
- Have solid ML and RL software engineering and scientific research skills
- Have produced novel research related to fine tuning SLMs / LLMs or improving upon pretrained SLMs / LLMs
- Proven experience in Gen AI libraries / frameworks, including but not limited to LangChain, LangGraph, LlamaIndex and LangSmith.
- At least 5 years of Research experience in one or more of these areas: NLP, speech, computer vision, Multimodal, machine learning, deep learning, or related fields.
- Proficiency in programming languages such as Python, and experience with NLP/ML frameworks like TensorFlow, PyTorch, and Hugging Face, as evidenced by released code (e.g. on GitHub) or elsewhere
- Familiarity with domain specific applications and the ability to translate domain knowledge into effective language models.
- Knowledge of Responsible AI practices and compliance with relevant laws is a plus.
- Industry experience with large-scale data processing, distributed computing, or cloud platforms.
- Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms is a plus.
- Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse audiences.
- Ability to work collaboratively in a team environment and contribute to a positive and productive work culture.

Job Location: Chennai, Bangalore, Noida, Hyderabad

As a Gen AI Researcher, you will develop and optimize domain specific large language models (LLMs) and small language models (SLMs) to address unique domain specific challenges and deliver innovative solutions. Your role includes creating advanced SLMs and LLMs for specific domains, focusing on supervised fine tuning, reinforcement learning from human feedback (RLHF), pre and post training, evaluation, and multi-modalities.

Key Responsibilities:

• Conduct cutting edge research in the field of NLP and develop advanced SLMs and LLMs tailored to domain specific applications.
• Design, implement, and optimize language models to solve domain specific problems effectively and efficiently.
• Analyze and preprocess large datasets to ensure high-quality input for training SLMs and LLMs.
• Design and Evaluate the performance of language models using appropriate metrics and benchmarks, and iterate on improvements.
• Developing and implementing methods that extend and improve model capabilities, reliability, and safety
• Work closely with cross functional teams, including domain experts, data engineers, and software engineers, to integrate language models into the production.
• Publish research findings in top tier conferences and journals, and communicate results to both technical and non technical stakeholders.
• Keep abreast of the latest advancements in the field of NLP, ML, and AI, and apply relevant techniques to ongoing projects.

Qualifications:

• Ph.D. in Computer Science, Computational Linguistics , Data Science, Artificial Intelligence, or a related field with a focus on NLP or ML.
• Bachelor's or Master’s degree in Computer Science, Computer Engineering, relevant technical field.
• At least 1 year of experience in developing and optimizing SLMs and LLMs, focusing on foundation model training, supervised fine tuning, reinforcement learning from human feedback (RLHF).
• Solid understanding of various fine-tuning techniques like full fine tuning, PEFT techniques like LoRA, QLoRA and the strategy to adopt for various objectives
• Have solid ML and RL software engineering and scientific research skills
• Have produced novel research related to fine tuning SLMs / LLMs or improving upon pretrained SLMs / LLMs
• Proven experience in Gen AI libraries / frameworks, including but not limited to LangChain, LangGraph, LlamaIndex and LangSmith.
• At least 5 years of Research experience in one or more of these areas: NLP, speech, computer vision, Multimodal, machine learning, deep learning, or related fields.
• Proficiency in programming languages such as Python, and experience with NLP/ML frameworks like TensorFlow, PyTorch, and Hugging Face, as evidenced by released code (e.g. on GitHub) or elsewhere
• Familiarity with domain specific applications and the ability to translate domain knowledge into effective language models.
• Knowledge of Responsible AI practices and compliance with relevant laws is a plus.
• Industry experience with large-scale data processing, distributed computing, or cloud platforms.
• Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms is a plus.
• Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse audiences.
• Ability to work collaboratively in a team environment and contribute to a positive and productive work culture.

About the Role

QpiAI works at the intersection of AI and Quantum Computing, developing groundbreaking solutions to tackle some of the most complex challenges across various industries. Our team is dedicated to pushing the boundaries of innovation and delivering exceptional results to our enterprise clients, driving significant impact.

We are seeking an exceptional Applied AI Researcher to join our innovative research team. This role will focus on advancing our AI capabilities through rigorous experimentation, multi-agent systems design, domain-specific scaffolding, and evaluation methodology development. Through this role you will get the opportunity to develop Compound AI systems impacting areas like Drug Discovery, Decision Intelligence, Chip Design , Quantum Computing etc.

Key Responsibilities

• Design and implement novel multi-agent architectures that enable complex problem-solving and collaboration
• Develop domain-specific scaffolding techniques to guide AI systems in specialized environments
• Create and curate high-quality datasets for training and evaluating AI systems across various domains
• Establish comprehensive evaluation frameworks to measure system performance, robustness, and alignment
• Research and implement reinforcement learning approaches for agent behavior optimization
• Develop techniques for post-training optimization and domain adaptation
• Collaborate with cross-functional teams to translate research findings into practical applications
• Stay current with the latest advancements in AI research and contribute to the research community
• Document methodologies, findings, and best practices for internal knowledge sharing

Qualifications

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or related technical field
• 4+ years of experience in applied AI research
• Strong mathematical foundations in optimization, probability theory, and linear algebra
• Extensive expertise in Python programming and software development
• Strong proficiency in PyTorch or JAX for implementing and scaling AI models
• Experience in curating datasets, pre-training, and aligning multimodal small language models
• Demonstrated experience with various reinforcement learning post-training methods (RLHF, DPO, GRPO, SFT, etc.)
• Experience with prompting methods like Chain of Thought and curating Chain of Thought datasets
• Experience training small reasoning models for specific applications
• Familiarity with various alignment methods and value learning techniques
• Experience with multi-agent systems design and implementation
• Knowledge of distributed training on multi-node, multi-GPU clusters
• Experience with dataset curation, cleaning, and augmentation for diverse AI tasks
• Strong understanding of model evaluation metrics and methodology development
• Excellent communication skills and ability to present complex technical concepts clearly

Preferred Experience

• Publication record in top-tier AI/ML conferences (NeurIPS, ICLR, AAMAS, ICML)
• Experience applying AI to scientific domains such as drug discovery, materials science, chip design, or mathematical reasoning
• Experience working with vision-language models and multimodal architectures
• Demonstrated success in developing efficient small language models for specialized tasks
• Research experience in open-endedness, including emergent behaviors, auto-curriculum learning, or open-world agent systems for design of experiments, hypothesis generation.
• Background in computational chemistry, physics, electrical engineering, or mathematics
• Experience with Kubernetes and containerized workflow orchestration
• Familiarity with ML infrastructure tools like Ray, Hydra, or MLflow
• Knowledge of interpretability methods and system analysis approaches
• Experience with domain adaptation for specialized scientific applications
• Open-source contributions to AI research or tooling
• Experience in developing custom GPU accelerated pipelines.
• Experience optimizing large-scale models for inference and deployment.

What We Offer

• Opportunity to work on cutting-edge AI research with real-world impact
• Access to substantial computational resources for large-scale experimentation
• Collaborative environment with top researchers and engineers
• Flexibility to pursue novel research directions within the company's mission
• Competitive compensation and benefits package

Join us in shaping the future of intelligent systems and their applications across diverse scientific domains.

Job Description

What if your RL research didn’t just end in a paper, but helped autonomous aircraft make split-second decisions in places where GPS fails, comms go dark, and backup isn’t coming?

That’s the mission on the table, and it needs someone who’s ready to push reinforcement learning into the real world.

A deep tech startup is building autonomous VTOL systems for contested environments. And they’re hiring someone with serious research skills, someone who knows RL and wants to apply it where it actually matters.

Not to optimise ads. Not to fine-tune chatbot responses. But to guide high-stakes autonomous systems through uncertainty and dynamic environments.

The Role

You’ll be working on long-horizon tactical decision-making using RL and related methods, from reward design and policy training, to sim tuning and field testing.

You’ll collaborate with researchers and engineers on multi-agent problems, environment modelling, and real-world autonomy challenges. And yes, if publishing is important to you, you’ll be supported to keep doing it.

This isn’t a siloed postdoc. It’s a full-time, permanent role inside a company that values both deep thinking and functional systems.

Who This Might Suit

Maybe you’re just wrapping up a PhD in machine learning, robotics, or AI.

Maybe you’ve already done a postdoc but want to escape the 2-year treadmill.

Or maybe you’re an engineer with serious RL chops and no interest in the academic track, just the desire to build something that flies.

Either way, you should:

• Be confident reading RL papers and turning them into working code

• Understand how to shape reward functions and work with MDPs/POMDPs

• Care about how your work performs outside of simulation

• Be eligible to work in Australia (Citizenship or PR is a must)

• Be willing to work onsite (Melbourne or Adelaide)

Why It’s Different

• Full-time, permanent role

• Real systems, not just simulation or theory

• Room to grow - either into R&D leadership or deep technical IC

• High-agency, low-politics environment

• A team that values capability over credentials

Curious?

You don’t need to submit a polished CV to get started. Just reach out to Thaís on and we’ll take it from there.

Job Location: Chennai, Bangalore, Noida, Hyderabad

As a Gen AI Researcher, you will develop and optimize domain specific large language models (LLMs) and small language models (SLMs) to address unique domain specific challenges and deliver innovative solutions. Your role includes creating advanced SLMs and LLMs for specific domains, focusing on supervised fine tuning, reinforcement learning from human feedback (RLHF), pre and post training, evaluation, and multi-modalities.

Key Responsibilities:

• Conduct cutting edge research in the field of NLP and develop advanced SLMs and LLMs tailored to domain specific applications.
• Design, implement, and optimize language models to solve domain specific problems effectively and efficiently.
• Analyze and preprocess large datasets to ensure high-quality input for training SLMs and LLMs.
• Design and Evaluate the performance of language models using appropriate metrics and benchmarks, and iterate on improvements.
• Developing and implementing methods that extend and improve model capabilities, reliability, and safety
• Work closely with cross functional teams, including domain experts, data engineers, and software engineers, to integrate language models into the production.
• Publish research findings in top tier conferences and journals, and communicate results to both technical and non technical stakeholders.
• Keep abreast of the latest advancements in the field of NLP, ML, and AI, and apply relevant techniques to ongoing projects.

Qualifications:

• Ph.D. in Computer Science, Computational Linguistics , Data Science, Artificial Intelligence, or a related field with a focus on NLP or ML.
• Bachelor's or Master’s degree in Computer Science, Computer Engineering, relevant technical field.
• At least 1 year of experience in developing and optimizing SLMs and LLMs, focusing on foundation model training, supervised fine tuning, reinforcement learning from human feedback (RLHF).
• Solid understanding of various fine-tuning techniques like full fine tuning, PEFT techniques like LoRA, QLoRA and the strategy to adopt for various objectives
• Have solid ML and RL software engineering and scientific research skills
• Have produced novel research related to fine tuning SLMs / LLMs or improving upon pretrained SLMs / LLMs
• Proven experience in Gen AI libraries / frameworks, including but not limited to LangChain, LangGraph, LlamaIndex and LangSmith.
• At least 5 years of Research experience in one or more of these areas: NLP, speech, computer vision, Multimodal, machine learning, deep learning, or related fields.
• Proficiency in programming languages such as Python, and experience with NLP/ML frameworks like TensorFlow, PyTorch, and Hugging Face, as evidenced by released code (e.g. on GitHub) or elsewhere
• Familiarity with domain specific applications and the ability to translate domain knowledge into effective language models.
• Knowledge of Responsible AI practices and compliance with relevant laws is a plus.
• Industry experience with large-scale data processing, distributed computing, or cloud platforms.
• Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms is a plus.
• Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse audiences.
• Ability to work collaboratively in a team environment and contribute to a positive and productive work culture.