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 client’s 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.
Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it.
Understanding and relating technology integration scenarios and applying these learnings in projects.
Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
Carrying out POCs to make sure that suggested design/technologies meet the requirements.