Experience :

- 5+ years of experience in Machine Learning, AI, or Data Science roles with production deployments.

- Bachelors or Masters in Computer Science, AI/ML, Data Science, Statistics, Mathematics, or a related quantitative field.

- Demonstrated success in applying and deploying AI/ML solutions to solve real-world, business-critical problems.

Technical Expertise :

- Strong proficiency in Python with production-quality coding standards.

- Hands-on experience with ML frameworks : PyTorch, TensorFlow, scikit-learn, Hugging Face.

- Experience with LLMs and GenAI Agent design and tooling.

- Experience and deep knowledge of vector databases and services.

- Experience with experiment tracking and MLOps tools : MLflow, Weights & Biases, TensorBoard.

- Solid understanding of SQL and data processing at scale.

- Experience with Kubernetes (AKS).

Deployment & Infrastructure :

- Experience with model serving frameworks (vLLM, Triton, TensorRT, ONNX Runtime).

- Hands-on experience with Docker and Kubernetes in production environments.

- Comfortable with setting up CI/CD pipelines for AI/ML workflows.

Soft Skills :

- Strong problem-solving and analytical thinking.

- Excellent communication skills with the ability to engage both technical and business stakeholders.

- Strong stakeholder management and a passion for building high-performing teams.

Nice to Have :

- Experience with Azure tech stack, Azure AI Foundry.

- Experience and understanding of working with fine-tuning techniques (LoRA/QLoRA etc.) and inference optimization tools (vLLM, TensorRT, ONNX Runtime).

- Experience with Data engineering best practices and pipeline creation.

- Healthcare domain knowledge (EHR, FHIR, HL7).

- Experience with GPU optimization, CUDA, or distributed training (DeepSpeed).

- Knowledge of responsible AI practices for AI Agents and services : RAI Guidelines and regulatory compliance.

- Experience with graph-based ML, reinforcement learning, or federated learning.