493 Deep Learning jobs in Noida

Job Title: Deep Learning & Computer Vision Engineer

Company: Confidential

Location: On-site (Delhi)

Employment Type: Full-Time

Experience: 3–5 Years

Role Overview

We are seeking a Deep Learning & Computer Vision Engineer to design, develop, and deploy AI-powered solutions. You will be responsible for building real-time computer vision pipelines, implementing state-of-the-art deep learning models, and bringing research concepts into production. This is an exciting opportunity to work in a fast-paced startup environment where your work directly shapes innovative products.

Key Responsibilities

• Design and implement deep learning models for image classification, object detection, segmentation, and tracking.
  
• Develop computer vision pipelines for real-time applications.
  
• Research and apply the latest AI and computer vision techniques from research papers to production systems.
  
• Deploy, optimize, and maintain models on cloud platforms (AWS, GCP, Azure).
  
• Work with large datasets, performing preprocessing and data augmentation .
  
• Collaborate with cross-functional teams to integrate AI into products.
  
• Monitor model performance and improve efficiency.
  
• Mentor junior engineers and support team knowledge sharing.
  

Required Skills & Qualifications

• 3–5 years of experience in deep learning and computer vision .
  
• Proficiency in Python and frameworks like TensorFlow, PyTorch, Keras .
  
• Strong understanding of OpenCV and computer vision algorithms.
  
• Hands-on experience with neural architectures (CNNs, YOLO, ResNet, Transformers ).
  
• Experience deploying models on AWS/GCP/Azure and knowledge of MLOps (Docker, Git, CI/CD).
  
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  

Preferred Qualifications

• Experience with ROS and robotics applications.
  
• Knowledge of model optimization techniques (quantization, pruning).
  

Job Title: Deep Learning & Computer Vision Engineer

Company: Confidential

Location: On-site (Delhi)

Employment Type: Full-Time

Experience: 3–5 Years

Role Overview

We are seeking a Deep Learning & Computer Vision Engineer to design, develop, and deploy AI-powered solutions. You will be responsible for building real-time computer vision pipelines, implementing state-of-the-art deep learning models, and bringing research concepts into production. This is an exciting opportunity to work in a fast-paced startup environment where your work directly shapes innovative products.

Key Responsibilities

• Design and implement deep learning models for image classification, object detection, segmentation, and tracking.
  
• Develop computer vision pipelines for real-time applications.
  
• Research and apply the latest AI and computer vision techniques from research papers to production systems.
  
• Deploy, optimize, and maintain models on cloud platforms (AWS, GCP, Azure).
  
• Work with large datasets, performing preprocessing and data augmentation .
  
• Collaborate with cross-functional teams to integrate AI into products.
  
• Monitor model performance and improve efficiency.
  
• Mentor junior engineers and support team knowledge sharing.
  

Required Skills & Qualifications

• 3–5 years of experience in deep learning and computer vision .
  
• Proficiency in Python and frameworks like TensorFlow, PyTorch, Keras .
  
• Strong understanding of OpenCV and computer vision algorithms.
  
• Hands-on experience with neural architectures (CNNs, YOLO, ResNet, Transformers ).
  
• Experience deploying models on AWS/GCP/Azure and knowledge of MLOps (Docker, Git, CI/CD).
  
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  

Preferred Qualifications

• Experience with ROS and robotics applications.
  
• Knowledge of model optimization techniques (quantization, pruning).
  
• Requirements
  Required Skills & Qualifications 3–5 years of experience in deep learning and computer vision. Proficiency in Python and frameworks like TensorFlow, PyTorch, Keras. Strong understanding of OpenCV and computer vision algorithms. Hands-on experience with neural architectures (CNNs, YOLO, ResNet, Transformers). Experience deploying models on AWS/GCP/Azure and knowledge of MLOps (Docker, Git, CI/CD). Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Preferred Qualifications Experience with ROS and robotics applications. Knowledge of model optimization techniques (quantization, pruning).

• Conducting original research in deep learning, natural language processing, computer vision, or reinforcement learning.
• Developing and implementing novel AI algorithms and models to solve challenging problems.
• Designing and executing experiments to evaluate model performance and identify areas for improvement.
• Collaborating with engineering teams to integrate research prototypes into production systems.
• Staying abreast of the latest advancements in AI and machine learning through literature review and conference participation.
• Publishing research findings in top-tier academic conferences and journals.
• Mentoring junior researchers and interns.
• Contributing to the intellectual property portfolio of the company.
• Defining research roadmaps and strategic directions for AI initiatives.
• Presenting research findings to both technical and non-technical audiences.
• Working with large datasets, including data preprocessing, feature engineering, and model training.
• Evaluating and selecting appropriate tools and frameworks for AI development.

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.

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.