AI/ML Lead

Educational Qualification:

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

2. Advanced certifications in AI/ML (e.g., TensorFlow Developer, AWS ML Engineer) preferred.

3. Research papers, case studies, or significant open source contributions(are preferred)

Experience:

1. 710 years of overall professional experience in AI/ML solution development.

2. At least 45 years leading AI/ML model implementation teams.

3. 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 NeGD's Unified Al Delivery Team.

2. Develop reusable model components for classification, prediction, summarization, or 2. image analytics.

3. Evaluate open-source and proprietary AI models for fitment to government datasets.

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

5. Collaborate with MLOps teams to automate model 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, model evaluation techniques, and ethical AI practices.

Technical Competencies:

1. AI/ML Expertise: Deep learning architectures (CNN, RNN, Transformers), reinforcement learning, transfer learning, and foundation model fine-tuning

2. Model Development: TensorFlow, PyTorch, Hugging Face, MLflow, advanced hyperparameter tuning, and neural architecture search

3. Programming Languages: Python (expert level), R for statistical modeling, C++ for performance optimization, CUDA for GPU programming

4. Model Optimization: Quantization, pruning, knowledge distillation, ONNX, TensorRT, and inference optimization techniques

5. Cloud Al Platforms: AWS SageMaker, Azure ML, GCP Vertex Al, distributed training, and cloud-native AI architectures

6. MLOps& Deployment: Kubernetes, Docker, model serving (TorchServe, TensorFlow Serving), CVCD for ML, and production monitoring

7. Research & Innovation: Literature review, experimental design, research methodology, and emerging AI technology evaluation

8. Specialized AI: NLP (BERT, GPT, LLM fine-tuning), Computer Vision (YOLO, ResNet), Time Series (LSTM, Prophet), and Generative AI