355 Deep Learning jobs in Chennai

Internship | Hybrid/Remote

We're offering an exciting opportunity to work on real healthcare challenges using AI.

What You'll Do

• Support model development for medical imaging or predictive healthcare use cases
• Work with real datasets to clean, preprocess, and experiment on small-scale models
• Collaborate with the AI team to research, test, and improve model performance
• Document learnings and contribute to prototypes

What We're Looking For

• Solid Python fundamentals and hands-on experience with PyTorch or TensorFlow
• Exposure to machine learning concepts (via coursework or projects)
• Curiosity to explore AI in real-world healthcare settings
• Bonus: Knowledge of OpenCV, EDA, or Kaggle participation

Send your resume to

We are hiring for Deep Learning Engineer with 4+ years of exp for Chennai location.

If Interested ,kindly share the updated resume to

Required Competencies:

Experience in AI, Machine learning, Deep learning software frameworks Pytorch/Tensorflow/Keras.

Strong technical Knowledge on CNN, RNN and other related ML algorithms

Prior experience with Linux environment and computer vision frameworks/libraries (OpenCV) is desired

Solid programming experience in Python and C++

Familiar with development tools: GIT, CI (Gitlab CI / Jenkins), Linux

Basic understanding of autonomous mobile robots, autonomous vehicles, sensors, and the core elements of autonomy

Experience:

BE/ME/PhD in Computer Science Engineering, Robotics Engineering, Mechatronics Engineering or a related field

with 4+ years of experience on delivering products or solutions that utilized Computer Vision

• Lead the end-to-end development lifecycle of deep learning models, from data preprocessing and feature engineering to model training, evaluation, and deployment.
• Design and implement novel deep learning architectures for applications such as image recognition, object detection, natural language understanding, and generation.
• Conduct rigorous research and experimentation to identify and implement state-of-the-art algorithms and techniques.
• Develop robust data pipelines and ensure the quality and integrity of training data.
• Optimize model performance for efficiency, accuracy, and scalability in production environments.
• Collaborate closely with product managers, software engineers, and data scientists to translate business requirements into technical solutions.
• Mentor and guide a team of AI/ML engineers, fostering technical growth and best practices.
• Stay current with the latest advancements in AI, machine learning, and deep learning research.
• Contribute to the development of the company's AI strategy and technical roadmap.
• Document research findings, model designs, and implementation details thoroughly.

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• Minimum of 8 years of hands-on experience in AI/ML development, with at least 4 years focused specifically on deep learning.
• Proven expertise in developing and deploying deep learning models using frameworks such as TensorFlow, PyTorch, or Keras.
• Strong proficiency in Python and relevant libraries (e.g., NumPy, Pandas, Scikit-learn).
• Experience with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Demonstrated experience in leading technical projects and mentoring engineering teams.
• Excellent understanding of machine learning algorithms, statistical modeling, and data mining techniques.
• Strong problem-solving skills and the ability to work independently in a remote setting.
• Excellent communication and presentation skills, capable of explaining complex technical concepts to diverse audiences.

• Conduct advanced research in deep learning, machine learning, and artificial intelligence.
• Develop, implement, and evaluate novel deep learning architectures and algorithms for various applications.
• Design and execute experiments to test hypotheses and validate research findings.
• Stay abreast of the latest advancements in AI, machine learning, and deep learning literature and trends.
• Publish research findings in leading AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ACL) and journals.
• Collaborate with engineers to integrate research models into production systems.
• Mentor junior researchers and contribute to the overall scientific direction of the team.
• Develop prototypes and proof-of-concepts for new AI-driven features and products.
• Analyze large datasets to identify patterns, build predictive models, and extract insights.
• Contribute to the development of intellectual property through patents and publications.
• Communicate complex technical concepts effectively to both technical and non-technical audiences.
• Participate in the review of research papers and proposals.
• Ensure reproducible research through rigorous documentation and code management.
• Contribute to the ethical development and deployment of AI technologies.
• Identify and explore new research areas with high potential impact.

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• Minimum of 5-7 years of post-doctoral and/or industry research experience in deep learning.
• Proven track record of impactful research with publications in top-tier AI conferences and journals.
• Expertise in one or more areas of deep learning, such as NLP, computer vision, generative models, or reinforcement learning.
• Strong programming skills in Python and proficiency with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Experience with data preprocessing, model training, and evaluation techniques.
• Solid understanding of linear algebra, calculus, probability, and statistics.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong communication and collaboration skills, with the ability to work effectively in a remote team.
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure) is a plus.
• Ability to work independently and manage research projects from conception to completion.

Greetings from TCS!

TCS is Hiring for ML Engineer (Deep Learning/Generative AI)

Interview Mode: Virtual

Required Experience: 10-16 years

Work location: PAN INDIA

Required Skills:

• Degree in computer science, artificial intelligence, or IT.
• Strong knowledge of AI methodologies, including generative models, machine learning (ML), reinforcement learning, and natural language processing (NLP).
• Design, implement, and optimize generative AI architectures and models.
• Collaborate with data scientists and analysts to collect, preprocess, and manage large datasets, ensuring data quality and integrity for model training.
• Collaborate with cross-functional teams to integrate AI models into production environments, ensuring seamless deployment and operational efficiency.
• Fine-tune and optimize AI models for specific applications, focusing on improving accuracy, efficiency, and scalability.
• Experience of cloud-based platforms (e.g., AWS, Azure, GCP) to develop and deploy scalable AI solutions, ensuring high availability and performance.
• Implement MLOps best practices for continuous integration and deployment (CI/CD) of AI models, including monitoring, logging, and version control.
• Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).
• Experience in developing and deploying AI/Gen AI based systems in production.
• Provide technical guidance and mentorship to junior engineers and team members, fostering a culture of knowledge sharing and continuous learning.

Pattern is developing a cutting-edge AI drug discovery engine that combines AlphaFold/OpenFold structural predictions, generative molecular design, and reinforcement learning agents to navigate the ~10^60 possibilities in small-molecule chemical space.

We are seeking a Deep Learning Lead to architect, train, and deploy machine learning models for protein–ligand structure prediction, de novo molecular generation, and multi-objective optimization. You will partner closely with our Ligand Design & Pose Prediction Lead to integrate chemical and biological expertise into the model pipeline, ensuring our AI agents produce compounds that are potent, novel, and biologically relevant.

Key Responsibilities

Design and implement deep learning architectures for:

Pocket-conditioned molecular generation (e.g., Pocket2Mol, SE(3)-equivariant GNNs, 3D diffusion models).

Protein–ligand pose prediction (DiffDock, EquiBind, custom SE(3)-transformers).

Multi-objective reinforcement learning for compound optimization (potency, ADMET, novelty).

Fine-tune AlphaFold/OpenFold and related structure models for project-specific targets.

Integrate multi-modal biological context (from the Agentix Knowledge Graph) into generative and scoring models.

Develop and maintain scoring functions for binding affinity, selectivity, ADMET, and synthetic accessibility.

Implement uncertainty estimation and active learning loops to prioritize compound synthesis/testing.

Collaborate with the Ligand Design Lead to:

Translate medicinal chemistry insights into model constraints.

Incorporate wet-lab feedback into model retraining.

Co-develop workflows for real-time human–AI co-design.

Maintain MLOps pipelines for dataset versioning, model deployment, and experiment tracking.

Qualifications Required

PhD or MSc in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or related discipline.

2+ years of hands-on experience in deep learning model development, ideally in a scientific or molecular domain.

Strong expertise in generative modeling (transformers, diffusion models, graph neural networks).

Experience with 3D geometric deep learning for molecular structures (SE(3)-equivariant architectures).

Proficiency in reinforcement learning (policy gradients, model-based RL, quality-diversity search).

Strong programming skills in Python with frameworks like PyTorch or TensorFlow.

Experience in handling molecular datasets (PDB, ChEMBL, binding affinity data).

Preferred

Prior work on protein structure prediction, ligand docking, or molecular property prediction.

Familiarity with cheminformatics toolkits (RDKit, Open Babel).

Experience in integrating wet-lab assay data into active learning loops.

MLOps experience (Docker, CI/CD for ML, MLflow, DVC).

Why Join Us?

You’ll help define the AI engine at the heart of Pattern’s platform, working with cutting-edge molecular AI tools and direct structural chemistry input from our Ligand Design Lead. Your models will power a proprietary agentic search system designed to explore the chemical universe faster and smarter than competitors.

Pattern is developing a cutting-edge AI drug discovery engine that combines AlphaFold/OpenFold structural predictions, generative molecular design, and reinforcement learning agents to navigate the ~10^60 possibilities in small-molecule chemical space.

We are seeking a Deep Learning Lead to architect, train, and deploy machine learning models for protein–ligand structure prediction, de novo molecular generation, and multi-objective optimization. You will partner closely with our Ligand Design & Pose Prediction Lead to integrate chemical and biological expertise into the model pipeline, ensuring our AI agents produce compounds that are potent, novel, and biologically relevant.

Key Responsibilities

Design and implement deep learning architectures for:

Pocket-conditioned molecular generation (e.g., Pocket2Mol, SE(3)-equivariant GNNs, 3D diffusion models).

Protein–ligand pose prediction (DiffDock, EquiBind, custom SE(3)-transformers).

Multi-objective reinforcement learning for compound optimization (potency, ADMET, novelty).

Fine-tune AlphaFold/OpenFold and related structure models for project-specific targets.

Integrate multi-modal biological context (from the Agentix Knowledge Graph) into generative and scoring models.

Develop and maintain scoring functions for binding affinity, selectivity, ADMET, and synthetic accessibility.

Implement uncertainty estimation and active learning loops to prioritize compound synthesis/testing.

Collaborate with the Ligand Design Lead to:

Translate medicinal chemistry insights into model constraints.

Incorporate wet-lab feedback into model retraining.

Co-develop workflows for real-time human–AI co-design.

Maintain MLOps pipelines for dataset versioning, model deployment, and experiment tracking.

Qualifications Required

PhD or MSc in Computer Science, Machine Learning, Computational Chemistry, Bioinformatics, or related discipline.

2+ years of hands-on experience in deep learning model development, ideally in a scientific or molecular domain.

Strong expertise in generative modeling (transformers, diffusion models, graph neural networks).

Experience with 3D geometric deep learning for molecular structures (SE(3)-equivariant architectures).

Proficiency in reinforcement learning (policy gradients, model-based RL, quality-diversity search).

Strong programming skills in Python with frameworks like PyTorch or TensorFlow.

Experience in handling molecular datasets (PDB, ChEMBL, binding affinity data).

Preferred

Prior work on protein structure prediction, ligand docking, or molecular property prediction.

Familiarity with cheminformatics toolkits (RDKit, Open Babel).

Experience in integrating wet-lab assay data into active learning loops.

MLOps experience (Docker, CI/CD for ML, MLflow, DVC).

Why Join Us?

You’ll help define the AI engine at the heart of Pattern’s platform, working with cutting-edge molecular AI tools and direct structural chemistry input from our Ligand Design Lead. Your models will power a proprietary agentic search system designed to explore the chemical universe faster and smarter than competitors.