Description :

- Total experience 7+ years.

- Deep understanding of LLMs (e.g., GPTs, Llama, Claude, Gemini, Qwen, Mistral, BERT-family models) and their architectures (Transformers)

- Should have expert-level prompt engineering skills and proven experience implementing RAG patterns

- High proficiency in Python and standard AI/ML libraries (e.g., LangChain, LlamaIndex, LangGraph, LangSmith, Hugging Face Transformers, Scikit-learn, PyTorch/TensorFlow).

- Experience implementing RAG architectures and prompt engineering.

- Strong experience with fine-tuning and distillation techniques and evaluation.

- Strong experience using managed AI/ML services on the target cloud platform (e.g., Azure Machine Learning Studio, AI Foundry).

- Strong understanding of vector databases (e.g., Weaviate, Neo4j)

- Understanding of GenAI evaluation metrics (e.g., BLEU, ROUGE, perplexity, semantic similarity, human evaluation).

- Architect and implement scalable GenAI and Agentic AI solutions end-to-end.

- Should be able to write high-quality, production-ready Python code with strong testing and maintainability practices.

- Should be able to productionize AI systems on Azure or AWS, ensuring enterprise-grade reliability and performance.

- Should be able to build and expose APIs using FastAPI, integrating with databases through an ORM.

- Should be able to scale GenAI solutions to support enterprise workloads.

- Collaborate across product and engineering teams to convert business needs into AI-driven solutions.

- Strong ability to both architect and code GenAI/Agentic AI solutions.

- Proven production experience with GenAI deployments on Azure or AWS.

- Should be able to build & deploy AI pipelines using SageMaker, Vertex AI, or Azure ML

- Hands on Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Argo) for scalable AI infra

- Hands-on with serverless AI APIs, containerized model serving, and GPU orchestration

- Experience with IaC (Terraform / Bicep) and cloud monitoring tools

- Data pipelines via Airflow, Kafka, or Databricks

- Strong experience in scaling AI solutions in live environments.

- Very strong Python programming skills with a track record of clean, efficient, and maintainable code.

- Should have successfully delivered at least one production GenAI/Agentic AI solution.

- Must have proficiency with FastAPI and at least one ORM (e.g., SQLAlchemy, Tortoise ORM).

- Should have experience with Model Context Protocol (MCP).

- Should have contributions to open-source GenAI projects.

- Good to have experience with React (or some other JS frameworks) for building user-facing interfaces and front-end integrations

- Excellent communication skills and the ability to collaborate effectively with cross-functional teams

RESPONSIBILITIES :

- Understanding the clients business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.

- Mapping decisions with requirements and be able to translate the same to developers.

- Identifying different solutions and being able to narrow down the best option that meets the clients requirements.

- Defining guidelines and benchmarks for NFR considerations during project implementation.

- Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers.