Description

Key Responsibilities

1. Hands-on Development:

• Build and enhance Generative AI solutions using Azure OpenAI and other Azure AI tools.

• Develop RAG pipelines—chunking, embeddings, vector indexing, retrieval logic.

• Implement LLM-based applications using LangChain, Semantic Kernel, etc.

• Build extensions/plugins for Copilot (Copilot Studio, Graph Connectors).

• Create APIs and services supporting AI workflows.

2. Technical Leadership:

• Mentor engineering teams and guide implementation best practices.

• Conduct code reviews ensuring quality and security.

• Collaborate with product teams to align technical outputs with requirements.

3. Evaluation & Quality Assurance:

• Use evaluation frameworks like RAGAS or DeepEval.

• Establish processes for testing LLM outputs (accuracy, hallucinations, safety).

• Support CI/CD and monitoring of AI components.

4. Collaboration & Documentation:

• Work with platform, data, and ML teams for integration.

• Develop documentation and reusable AI components.

Required Qualifications

Technical Skills:

• Hands-on experience with Azure OpenAI, Cognitive Search, Azure ML, and related services.

• Strong skills in LLMs, RAG pipelines, embeddings, vector databases, and prompt engineering.

• Programming experience in Python , Fast API, React / TypeScript

• Should Have good experience on handling unstructured & structured data – including working with non-relational models & relational models.
• Should have experience in SQL, MySQL, Cosmos DB

• Familiarity with LangChain, Semantic Kernel, and vector DBs such as Pinecone or Redis Vector.

• Knowledge of Copilot extensibility (plugins, connectors).

• Experience using RAGAS or similar AI evaluation tools.

Soft Skills:

• Leadership and mentoring abilities.

• Strong debugging and analytical skills.

• Excellent communication skills.

Good-to-Have Skills

• Exposure to MLOps on Azure ML.

• Understanding of ETL/data preparation for RAG.

• Experience with multimodal AI (Vision, Document Intelligence, Speech).

• Familiarity with Responsible AI principles.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

• 7–12 years of software engineering experience with 2–3 years in AI/LLM projects.

• Prior technical leadership experience preferred

Key Skills

Education

Bachelor's or Master's degrees