AI/ML Lead
The Versatile ClubIndia3 days ago
Full-timeOther
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Key Responsibilities
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
Skills: ml,models,cloud ai platforms,model development,ai,ai/ml,ai/ml models,mlops & deployment:
- Lead the design, prototyping, and deployment of AI/ML models for NeGD's Unified AI
- Develop reusable model components for classification, prediction, summarization, or
- 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
- 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
- Mentor engineering teams in ML algorithms, model evaluation techniques, and ethical AI
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
Skills: ml,models,cloud ai platforms,model development,ai,ai/ml,ai/ml models,mlops & deployment:
Key Skills
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