5,280 Machine Learning Engineer jobs in India

Designation: - ML / MLOPs Engineer

Location: - Noida (Sector- 132)

Key Responsibilities:

• Model Development & Algorithm Optimization : Design, implement, and optimize ML

models and algorithms using libraries and frameworks such as TensorFlow , PyTorch , and

scikit-learn to solve complex business problems.

• Training & Evaluation : Train and evaluate models using historical data, ensuring accuracy,

scalability, and efficiency while fine-tuning hyperparameters.

• Data Preprocessing & Cleaning : Clean, preprocess, and transform raw data into a suitable

format for model training and evaluation, applying industry best practices to ensure data

quality.

• Feature Engineering : Conduct feature engineering to extract meaningful features from data

that enhance model performance and improve predictive capabilities.

• Model Deployment & Pipelines : Build end-to-end pipelines and workflows for deploying

machine learning models into production environments, leveraging Azure Machine

Learning and containerization technologies like Docker and Kubernetes .

• Production Deployment : Develop and deploy machine learning models to production

environments, ensuring scalability and reliability using tools such as Azure Kubernetes

Service (AKS) .

• End-to-End ML Lifecycle Automation : Automate the end-to-end machine learning

lifecycle, including data ingestion, model training, deployment, and monitoring, ensuring

seamless operations and faster model iteration.

• Performance Optimization : Monitor and improve inference speed and latency to meet real-

time processing requirements, ensuring efficient and scalable solutions.

• NLP, CV, GenAI Programming : Work on machine learning projects involving Natural

Language Processing (NLP) , Computer Vision (CV) , and Generative AI (GenAI) ,

applying state-of-the-art techniques and frameworks to improve model performance.

• Collaboration & CI/CD Integration : Collaborate with data scientists and engineers to

integrate ML models into production workflows, building and maintaining continuous

integration/continuous deployment (CI/CD) pipelines using tools like Azure DevOps , Git ,

and Jenkins .

• Monitoring & Optimization : Continuously monitor the performance of deployed models,

adjusting parameters and optimizing algorithms to improve accuracy and efficiency.

• Security & Compliance : Ensure all machine learning models and processes adhere to

industry security standards and compliance protocols , such as GDPR and HIPAA .

• Documentation & Reporting : Document machine learning processes, models, and results to

ensure reproducibility and effective communication with stakeholders.Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related

field.

• 3+ years of experience in machine learning operations (MLOps), cloud engineering, or

similar roles.

• Proficiency in Python , with hands-on experience using libraries such as TensorFlow ,

PyTorch , scikit-learn , Pandas , and NumPy .

• Strong experience with Azure Machine Learning services, including Azure ML Studio ,

Azure Databricks , and Azure Kubernetes Service (AKS) .

• Knowledge and experience in building end-to-end ML pipelines, deploying models, and

automating the machine learning lifecycle.

• Expertise in Docker , Kubernetes , and container orchestration for deploying machine

learning models at scale.

• Experience in data engineering practices and familiarity with cloud storage solutions like

Azure Blob Storage and Azure Data Lake .

• Strong understanding of NLP , CV , or GenAI programming, along with the ability to apply

these techniques to real-world business problems.

• Experience with Git , Azure DevOps , or similar tools to manage version control and CI/CD

pipelines.

• Solid experience in machine learning algorithms , model training , evaluation , and

hyperparameter tuning

Role: ML Engineer (Part time)

Experience- 2- 5 Years

Location: Hyderabad

Job description:

We're seeking a highly skilled Machine Learning Engineer to drive the development and implementation of cutting-edge ML solutions. As a genius with ML, you'll leverage your expertise to design, build, and deploy innovative models and algorithms that solve complex problems and drive business growth.

Key Responsibilities:

1. ML Model Development: Design, train, and deploy machine learning models using various techniques, including deep learning, natural language processing, and computer vision.

2. Innovation Leadership: Lead the development of innovative ML solutions, identifying opportunities for growth and improvement.

3. Collaboration: Work with cross-functional teams to integrate ML solutions into products and services.

4. Research: Stay up-to-date with the latest ML research and trends, applying findings to improve existing solutions and drive innovation.

Requirements:

1. Technical Expertise: Strong background in machine learning, deep learning, and programming languages such as Python, TensorFlow, or PyTorch.

2. Innovation Mindset: Proven ability to think creatively and develop innovative solutions.

3. Collaboration: Excellent communication and collaboration skills.

Nice to Have:

1. Cloud Experience: Experience with cloud platforms such as AWS, Azure, or Google Cloud.

2. Domain Expertise: Knowledge of specific domains, such as healthcare, finance, or computer vision.

What We Offer:

1. Challenging Projects: Opportunities to work on complex, high-impact projects.

2. Collaborative Environment: Dynamic team of experts in ML and related fields.

3. Growth Opportunities: Professional development and growth opportunities.

If you're passionate about machine learning and innovation, we'd love to hear from you!

Dear All,

We are seeking a highly capable Machine Learning Engineer with deep expertise in fine-tuning Large Language Models (LLMs) and Vision-Language Models (VLMs) for intelligent document processing. This role requires strong knowledge of PEFT techniques (LoRA, QLoRA), quantization , transformer architectures , prompt engineering , and orchestration frameworks like LangChain . You’ll work on building and scaling end-to-end document processing workflows using both open-source and commercial models (OpenAI, Google, etc.), with an emphasis on performance, reliability, and observability.

• Fine-tune and optimize open-source and commercial LLMs/VLMs (e.g., LLaMA,Cohere, Gemini, GPT-4) for structured and unstructured document processing tasks.
• Apply advanced PEFT techniques (LoRA, QLoRA) and model quantization to enable efficient deployment and experimentation.
• Design LLM-based document intelligence pipelines for tasks like OCR extraction, entity recognition, key-value pairing, summarization, and layout understanding.
• Develop and manage prompting techniques (zero-shot, few-shot, chain-of-thought, self-consistency) tailored to document use-cases.
• Implement LangChain -based workflows integrating tools, agents, and vector stores for RAG-style processing.
• Monitor experiments and production models using Weights & Biases (W&B) or similar ML observability tools.
• Work with OpenAI (GPT series) , Google PaLM / Gemini , and other LLM/VLM APIs for hybrid system design.
• Collaborate with cross-functional teams to deliver scalable, production-ready ML systems and continuously improve model performance.
• Build reusable, well-documented code and maintain a high standard of reproducibility and traceability.

• Hands-on experience with transformer architectures and libraries like HuggingFace Transformers.
• Deep knowledge of fine-tuning strategies for large models, including LoRA , QLoRA , and other PEFT approaches.
• Experience in prompt engineering and developing advanced prompting strategies.
• Familiarity with LangChain , vector databases (e.g., FAISS, Pinecone), and tool/agent orchestration.
• Strong applied knowledge of OpenAI , Google (Gemini/PaLM) , and other foundational LLM/VLM APIs.
• Proficiency in model training, tracking, and monitoring using tools like Weights & Biases (W&B) .
• Solid understanding of deep learning , machine learning , natural language processing , and computer vision concepts.
• Experience working with document AI models (e.g., LayoutLM, Donut, Pix2Struct) and OCR tools (Tesseract, EasyOCR, etc.).
• Proficient in Python , PyTorch , and related ML tooling.

• Experience with multi-modal architectures for document + image/text processing.
• Knowledge of RAG systems , embedding models , and custom vector store integrations.
• Experience in deploying ML models via FastAPI , Triton , or similar frameworks.
• Contributions to open-source AI tools or model repositories.
• Exposure to MLOps , CI/CD pipelines , and data versioning.

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

• Work on cutting-edge GenAI and Document AI use-cases.
• Collaborate in a fast-paced, research-driven environment.
• Flexible work arrangements and growth-focused culture.
• Opportunity to shape real-world applications of LLMs and VLMs.

Founded in the year 2017, CoffeeBeans specializes in offering high end consulting services in technology, product, and processes. We help our clients attain significant improvement in quality of delivery through impactful product launches, process simplification, and help build competencies that drive business outcomes across industries. The company uses new-age technologies to help its clients build superior products and realize better customer value. We also offer data-driven solutions and AI-based products for businesses operating in a wide range of product categories and service domains.

As part of our continued platform evolution and expansion into generative AI, we are looking for a skilled Machine Learning Engineer to join our team. You will be responsible for designing, building, and deploying machine learning models, with a focus on GenAI, Retrieval-Augmented Generation (RAG), large language models (LLMs), and intelligent chatbot integration. Your work will enable rapid development of innovative use cases contextualized to our client's domain, significantly reducing time-to-market and enhancing end-user experience.

Key Responsibilities:

• Design and develop machine learning and generative AI models to enhance platform capabilities, enable intelligent interactions, and support diverse use cases.
• Implement Retrieval-Augmented Generation (RAG) pipelines for contextual responses based on enterprise data.
• Integrate LLM-based chatbots with client-specific domain knowledge and data systems to ensure relevance and accuracy.
• Collaborate with Data Scientists, ML Engineers, and MLOps teams to operationalize models using Azure ML and other cloud-native services.
• Build and maintain scalable machine learning and RAG pipelines using Azure technologies.
• Fine-tune pre-trained LLMs and optimize model performance based on business and technical feedback.
• Continuously evaluate emerging techniques in generative AI, ML, and data science to recommend improvements and innovations.
• Work in an Agile environment and participate in team ceremonies and stakeholder interactions to align model development with business needs.

Required Skills and Experience:

• 4–9 years of relevant experience in AI/Machine Learning, Data Science, or a related field.
• Strong proficiency in Python and experience with ML libraries such as PyTorch, TensorFlow, HuggingFace Transformers, LangChain, etc.
• Hands-on experience with Azure ML, Azure Cognitive Services, Azure OpenAI, and related Azure data services (e.g., Azure Data Lake, Azure Functions, Azure Search).
• Experience building and deploying RAG architectures and working with LLMs for enterprise use cases.
• Understanding of vector databases (e.g., Pinecone, Weaviate, Azure AI Search with Vector Capabilities).
• Familiarity with chatbot development frameworks and integrating with front-end interfaces.
• Solid grasp of ML model lifecycle including versioning, monitoring, CI/CD, and MLOps principles.
• Experience working in collaborative, agile, and fast-paced environments.
• Databricks

Desirable Skills:

• Familiarity with large-scale data processing using Spark, or similar.
• Knowledge of containerization and orchestration tools like Docker and Kubernetes.
• Exposure to domain-specific fine-tuning of LLMs and prompt engineering practices.
• Strong communication and stakeholder collaboration skills to understand client context and explain technical approaches effectively.

Must Have Requirements

• Strong proficiency in Python and experience with ML libraries such as PyTorch, TensorFlow, HuggingFace Transformers, LangChain, etc.
• BFSI Domain Experience

About the Role:

We are seeking an experienced MLOps Engineer to lead the deployment, scaling, and performance optimization of open-source Generative AI models on cloud infrastructure. You’ll work at the intersection of machine learning, DevOps, and cloud engineering to help productize and operationalize large-scale LLM and diffusion models.

Key Responsibilities:

• Design and implement scalable deployment pipelines for open-source Gen AI models (LLMs, diffusion models, etc.).
• Fine-tune and optimize models using techniques like LoRA, quantization, distillation, etc.
• Manage inference workloads, latency optimization, and GPU utilization.
• Build CI/CD pipelines for model training, validation, and deployment.
• Integrate observability, logging, and alerting for model and infrastructure monitoring.
• Automate resource provisioning using Terraform, Helm, or similar tools on GCP/AWS/Azure.
• Ensure model versioning, reproducibility, and rollback using tools like MLflow, DVC, or Weights & Biases.
• Collaborate with data scientists, backend engineers, and DevOps teams to ensure smooth production rollouts.

Required Skills & Qualifications:

• 5+ years of total experience in software engineering or cloud infrastructure.
• 3+ years in MLOps with direct experience in deploying large Gen AI models.
• Hands-on experience with open-source models (e.g., LLaMA, Mistral, Stable Diffusion, Falcon, etc.).
• Strong knowledge of Docker, Kubernetes, and cloud compute orchestration.
• Proficiency in Python and familiarity with model-serving frameworks (e.g., FastAPI, Triton Inference Server, Hugging Face Accelerate, vLLM).
• Experience with cloud platforms (GCP preferred, AWS or Azure acceptable).
• Familiarity with distributed training, checkpointing, and model parallelism.

Good to Have:

• Experience with low-latency inference systems and token streaming architectures.
• Familiarity with cost optimization and scaling strategies for GPU-based workloads.
• Exposure to LLMOps tools (LangChain, BentoML, Ray Serve, etc.).

Why Join Us:

• Opportunity to work on cutting-edge Gen AI applications across industries.
• Collaborative team with deep expertise in AI, cloud, and enterprise software.
• Flexible work environment with a focus on innovation and impact.

Machine Learning Engineer:

Experience-5+ Yrs.

Location-Pune/Bangalore/Hyderabad/Chennai

JD-

• Expert-level proficiency in Google Cloud Platform (GCP) , demonstrating deep practical experience with Vertex AI , Big Query, Apache Beam, Cloud Storage, Pub/Sub, Cloud Composer (Apache Airflow), Cloud Run, Kubernetes Engine (GKE) concepts (for custom model serving), Docker.
• Strong experience leveraging GPUs/TPUs for accelerated ML training.
• Mastery of Python, TensorFlow and/or PyTorch, NLP libraries (e.g. spaCy, NLTK).
• Large-scale model training techniques, including distributed training, transfer learning, fine-tuning pre-trained models, and efficient data loading strategies
• Develop, fine-tune, and deploy LLMs using Vertex AI and GCP-native tools .
• Build and maintain NLP pipelines for tasks such as text classification, NER, question answering, summarization, and translation.
• Implement prompt engineering and retrieval-augmented generation (RAG) for enterprise applications.

Headquartered in Finland, Tecnotree Corporation is a leading provider of full-stack Digital BSS for CSPs and DSPs. Founded in 1978, Tecnotree helps customers to monetize and transform their business towards a marketplace of digital services. Together with its customers, Tecnotree empowers people to self-serve, engage, and take control of their own digital life. Tecnotree is listed on Nasdaq Helsinki (TEM1V).

This is a full-time hybrid role for a Machine Learning Engineer, located in Bengaluru with some work from home flexibility. The Machine Learning Engineer will be responsible for designing and implementing scalable machine learning models, developing pattern recognition solutions, creating neural networks, and applying statistical methods. Day-to-day tasks also include algorithm development, data analysis, and collaboration with cross-functional teams.

• Expertise in Pattern Recognition and Neural Networks
• Strong background in Computer Science and Statistics
• Proficiency in developing Algorithms
• Excellent problem-solving and analytical skills
• Ability to work independently and as part of a team
• Experience with machine learning frameworks and tools
• Bachelor's or Master's degree in Computer Science, Data Science, or a related field
• Experience in the telecom or digital services industry is a plus

About Eximietas:

Eximietas Design is a leading technology consulting and solutions development firm specializing in Chip design, Firmware & Embedded Software development, Cloud Computing, cybersecurity, and AI/ML domains. Our success is anchored in the unparalleled expertise of our engineering leadership team, who have collectively taped-out over 100+ chips and released countless software solutions for renowned tech giants like Google, Cisco, Microsoft, Oracle, Uber, Broadcom, and Sun. With a commitment to innovation and excellence, we deliver cutting-edge solutions that empower businesses to thrive in the ever-evolving digital landscape. We are an ISO 9001 and ISO 27001 certified company with development centers in the US and India.

Website link:

Please drop your updated resume at

Experience - 4-5years of (Relevant)

Location: Bangalore ( WFO - 5 days )

Job Type: Full-time

We are looking for candidates who have experience in MLOps and DevOps.

Skill Area-

Python

Software Testing (pytest)

Docker

RunAI / Kubernetes

Git & CI/CD (GitHub Actions)

GCP (Cloud Batch, GCR, etc.)

Model Conversion (ONNX, etc.)

Experiment Tracking (W&B, MLflow)

Hydra / Config Management

Hands-on experience in -

Cloud technologies

CI/CD and testing

MLOps tools and practices

Model tracking and versioning

Kubernetes and container orchestration

Interview Process -

1st Interview - Virtual

2nd interview - Virtual

3rd Client interview - F2F at client location in Bangalore (Must)

Job Title: Machine Learning Engineer

Location: Bengaluru (Hybrid)

Experience: 2-6 years

About Wissen Infotech

Wissen Infotech has been a trusted leader in the IT Services industry for over 25 years, delivering high-quality solutions to a global clientele. Within Wissen, the AI Center of Excellence (AI-CoE) was conceptualized to drive cutting-edge research and innovation, enabling us to build our own products and intellectual property. This team focuses on solving complex business challenges using AI while setting new benchmarks for reliable and scalable AI solutions.

Position Overview

We are seeking a passionate Machine Learning Engineer to join our AI-CoE team. This is a unique opportunity for individuals who are software engineers at heart and are driven to design, develop, and deploy robust AI systems. You will work on innovative projects, including building agentic systems, leveraging state-of-the-art technologies to create scalable and reliable distributed systems.

Key Responsibilities

• Design and develop scalable machine learning models and deploy them in production environments.

• Build and implement agentic systems that can autonomously analyze tasks, process large volumes of unstructured data, and provide actionable insights.

• Collaborate with data scientists, software engineers, and domain experts to integrate AI capabilities into cutting-edge products and solutions.

• Develop deterministic and reliable AI systems to address real-world challenges.

• Create and optimize scalable, distributed ML pipelines.

• Perform data preprocessing, feature engineering, and model evaluation to ensure high performance and reliability.

• Stay abreast of advancements in AI technologies and incorporate them into business solutions.

• Participate in code reviews, contribute to system architecture discussions, and continuously enhance project workflows.

Required Skills and Qualifications

• Software Engineering Fundamentals: Strong foundation in algorithms, data structures, and scalable system design.

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or related fields with a solid academic track record.

• Experience: 4-8 years of hands-on experience in building AI systems or machine learning applications.

• Agentic Systems: Proven experience in developing systems that utilize AI agents for automating complex workflows, analyzing unstructured data, and generating actionable outcomes.

• Programming: Proficiency in programming languages such as Python, Java, or Scala.

• AI Expertise: Experience with machine learning frameworks like TensorFlow, PyTorch, or Hugging Face libraries (e.g., for working with transformer-based models and LLMs).

• MLOps Knowledge (preferred): Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, or Kubernetes.

• Cloud Platforms: Hands-on experience with AWS, Azure, or Google Cloud for deploying machine learning models.

• Big Data: Experience with data processing tools and platforms such as Apache Spark or Hadoop.

• Problem-Solving: Strong analytical and problem-solving skills, with the ability to create robust solutions for complex challenges.

• Collaboration and Communication: Excellent communication skills to articulate technical ideas effectively to both technical and non-technical stakeholders.

What We Offer

• An opportunity to work with cutting-edge AI technologies and solve challenging business problems.

• A collaborative, innovative, and inclusive work culture.

• Continuous learning opportunities and access to advanced research.

• Competitive salary and comprehensive benefits