Role Summary
Embeds with client teams, understands manual business workflows, and builds AI-agent-powered automation to
replace them. Owns the solution end-to-end discovery, build, deploy, iterate working directly with clients with
minimal direction.
Agentic AI
LLM API fundamentals LLM API integration tool use, function calling, streaming, context management
Skill building Design and publish reusable skills consumable by LLM-powered agent workflows
MCP (Model Context Protocol) Build and consume MCP servers; expose and integrate tools for agent use
A2A communication Multi-agent coordination, task delegation, result aggregation across agent networks
RAG pipelines Vector databases, chunking, embeddings, retrieval pipelines
Prompt engineering System prompts, chain-of-thought, evaluation and iteration




Agent frameworks Strands as primary framework; awareness of LangGraph, CrewAI, or AutoGen
Guardrails Hallucination mitigation, human-in-the-loop checkpoints, agent observability
Backend
Python 3.10+, async/await, type hints
FastAPI or equivalent Python web framework
Basic SQL query, filter, join; awareness of vector DBs (pgvector, Pinecone, OpenSearch)
REST API design, JWT / OAuth2
Workflow Automation

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📌 Forward Deployed Engineer (Bengaluru)
🏢 Diverse Lynx
📍 Bengaluru