AI / ML Lead

Job Details

• Designation: AI / ML Lead
  

• Experience Required: 7–10 Years
  

• Open Positions: 2
  

• Salary: ₹2,33,750 per month
  

• Location: Delhi
  

Educational Qualification

• B.Tech / M.Tech / MS / Ph.D. in Computer Science, Artificial Intelligence, or related fields
  

• Advanced certifications in AI/ML such as TensorFlow Developer, AWS ML Engineer preferred
  

• Research papers, case studies, or significant open -source contributions preferred
  

Experience Required

• 7–10 years of overall professional experience in AI/ML solution development
  

• Minimum 4–5 years of experience leading AI/ML model implementation teams
  

• Proven experience delivering end -to -end AI/ML use cases from prototyping to production deployment
  

Key Responsibilities

1. Lead the design, prototyping, and deployment of AI/ML models for Unified AI Delivery Team projects
   

2. Develop reusable model components for classification, prediction, summarization, and image analysis
   

3. Evaluate open -source and proprietary AI models for suitability with government datasets
   

4. Define model development standards, performance benchmarks, and testing protocols
   

5. Collaborate with MLOps teams to automate deployment, retraining, and monitoring workflows
   

6. Guide Data Scientists in experiment setup, hyperparameter tuning, and model validation
   

7. Conduct technical reviews and ensure all models meet Responsible AI and data privacy requirements
   

8. Mentor engineering teams in ML algorithms, evaluation techniques, and ethical AI practices
   

Technical Competencies

AI / ML Expertise

• Deep learning architectures including CNN, RNN, Transformers
  

• Reinforcement learning, transfer learning, and foundation model fine -tuning
  

Model Development

• TensorFlow
  

• PyTorch
  

• Hugging Face
  

• MLflow
  

• Advanced hyperparameter tuning
  

• Neural architecture search
  

Programming Languages

• Python (Expert Level)
  

• R for statistical modeling
  

• C++ for performance optimization
  

• CUDA for GPU programming
  

Model Optimization

• Quantization
  

• Pruning
  

• Knowledge Distillation
  

• ONNX
  

• TensorRT
  

• Inference optimization techniques
  

Cloud AI Platforms

• AWS SageMaker
  

• Azure ML
  

• GCP Vertex AI
  

• Distributed training
  

• Cloud -native AI architectures
  

MLOps & Deployment

• Kubernetes
  

• Docker
  

• TorchServe
  

• TensorFlow Serving
  

• CI/CD for ML
  

• Production monitoring
  

Required Skills

• Artificial Intelligence
  

• Machine Learning
  

• Deep Learning
  

• TensorFlow
  

• PyTorch
  

• Hugging Face
  

• Python
  

• R
  

• C++
  

• CUDA
  

• MLflow
  

• AWS SageMaker
  

• Azure ML
  

• GCP Vertex AI
  

• Kubernetes
  

• Docker
  

• TensorFlow Serving
  

• TorchServe
  

• ONNX
  

• TensorRT
  

• CI/CD for ML
  

• MLOps
  

• Model Deployment
  

• Hyperparameter Tuning
  

• Neural Architecture Search
  

• Reinforcement Learning
  

• Transfer Learning
  

• CNN
  

• RNN
  

• Transformers
  

• Responsible AI
  

• Data Privacy
  

Preferred Candidate Profile

• Strong leadership and team management skills
  

• Excellent problem -solving and analytical abilities
  

• Ability to work on large -scale AI/ML transformation projects
  

• Strong communication and stakeholder management skills
  

• Understanding of Responsible AI and data privacy regulations