Description

Key Requirements:
Strong programming skills in Python (API development, asynchronous processing, modular architecture).
Hands-on experience with LLMs (OpenAI, Anthropic, Gemini, or open-source models).
In-depth understanding of RAG architecture and retrieval optimization techniques.
Practical development experience with LangChain and LangGraph.
Experience in building AI Agents / Agentic AI systems and multi-agent workflows.
Experience working with Vector Databases and the embeddings lifecycle.
Exposure to MCP (Model Context Protocol) implementation.
Experience with workflow orchestration tools (e.g., Airflow, Prefect, Temporal, Dagster, or equivalent).
Strong understanding of REST APIs, microservices, and event-driven architecture.
Familiarity with Docker, CI/CD pipelines, cloud platforms (AWS/Azure/GCP), and monitoring/logging tools.
Experience with guardrails, policy engines, and AI safety frameworks.
Exposure to graph-based retrieval, knowledge graphs, and hybrid search techniques.
Experience in developing domain-specific copilots/chatbots (e.g., banking, healthcare, telecom).
Basic frontend exposure (React) for AI assistant integration

Key Skills

Education

Bachelor's degree