AI / ML Lead: B.Tech / M.Tech / M.S. / Ph.D. in Computer Science, Artificial Intelligence, or related fields. . Advanced certifications in AI/ML (e.g., TensorFlow Developer, AWS ML Engineer) preferred.

- Research papers, case studies, or significant open source contributions(are preferred) Experience:

- 7-10 years of overall qualified experience in AI/ML solution development. . At least 4-5 years 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 NeGD's Unified AI Delivery Team.

- Develop reusable model components for classification, prediction, summarization, or image analytics.
- Evaluate open-source and proprietary AI models for fitment to government datasets.
- Define model development standards, performance benchmarks, and testing protocols.
- Collaborate with MLOps teams to automate model deployment, retraining, and monitoring workflows. 6.

Guide Data

Scientists in experiment setup, hyperparameter tuning, and model validation.

- Conduct technical reviews and ensure all models meet Responsible AI and data privacy requirements.
- Mentor engineering teams in ML algorithms, model evaluation techniques,



and ethical AI practices.

Technical Competencies: .

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

Model Development: TensorFlow, PyTorch, Hugging Face, MLflow, advanced hyperparameter tuning, and 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, and inference optimization techniques .

Cloud AI Platforms: AWS SageMaker, Azure ML, GCP Vertex AI, distributed training, and cloud-native AI architectures . MLOps& Deployment: Kubernetes, Docker, model serving (TorchServe, TensorFlow Serving), CI/CD for ML, and production monitoring.

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

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

Required Skills

['Python', 'AI/ML']

Additional Information

Use public JD while sourcing.

📌 AI / ML Lead (Bawana)
🏢 HuntingCube
📍 Bawana