- Project Role : Custom Software Engineering Lead

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- Project Role Description : Own the technical direction and architecture of custom software solutions, leading teams through design and delivery. Set development standards and ensure code quality, scalability, and performance aligned to business objectives.

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- Must have skills : AI Agents & Workflow Integration

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- Positive to have skills : AI & Data Solution Architecture

- Minimum 7.5 year(s) of experience is required

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- Educational Qualification : 15 years full time education

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- Summary: Minimum 2 years experiance in below skill

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- Platforms value is in its agents. Eight specialist services ?? five diagnostic and three planning ?? each call a combination of graph traversal, vector search, MCP tool invocation, and Bedrock reasoning to answer questions no human could answer quickly from raw a alone.

- This engineer builds Core, all eight agent services, the MCP Gateway, and the retrieval pipeline. The role that turns the knowledge graph into actionable intelligence. A second engineer joins at Week 9 when Phase 1 agent work opens in parallel with infrastructure.

- Responsibilities

- Build Core: the 13-node LangGraph StateGraph, hybrid BM25+vector retrieval pipeline, context packer with budget and provenance tracking, and Bedrock model router

- Implement all 8 agent services as FastAPI microservices: APM/Change Log, Historic RCA, Cloud Health, Integration Impacts, Log Analyzer, Change Impact, Designer, and Story Planner

- Build the Connector SDK base contract and migrate all 9 connectors to it (GitHub, Confluence, ServiceNow, JIRA, CMDB, SonarQube, Dynatrace, REMC, RepoMapping)

- Build and own the MCP Gateway: per-agent RBAC, request routing, rate limiting, token budget enforcement, and Postgres audit log of every tool call





- Implement HITL approval gates in both MI and CR orchestrators using LangGraph wire blast-radius threshold config and Slack approval shortcut

- Complete the MCP server set: Splunk MCP, Elasticsearch MCP, Slack MCP, and GoAlert write client

- Build and maintain the RAGAS evaluation harness as a continuous job instrument confidence-provenance vectors on every agent output

- Implement idempotent OpenCypher MERGE patterns, provenance flagging, and the freshness ledger

- Wire entity resolution output into the master index so all agent queries resolve to canonical IDs

- Contribute to the Phase 3 learning loop: agent-inferred edge proposal queue, human valiion UI, and graph write-back

- Required skills

- LangGraph and LangChain: multi-node stateful StateGraph design, conditional routing, HITL gate patterns, retry and fallback logic

- Python: async FastAPI service design, Pydantic models, production-grade error handling and structured logging

- LLM APIs: Amazon Bedrock (Claude Haiku, Sonnet, Titan v2), prompt engineering, token budget management, streaming responses

- Graph abases: OpenCypher queries against Neptune, idempotent MERGE patterns, traversal for blast-radius analysis

- Vector search: OpenSearch k-NN, Titan v2 embeddings, hybrid BM25+dense retrieval, relevance evaluation

- MCP (Model Context Protocol): server and client implementation, stdio and HTTP transport, tool schema design

- RAG evaluation: RAGAS metrics, confidence scoring, provenance tracking, retrieval quality measurement

- API integration: REST, OAuth 2.0, webhook receivers, rate limit handling across enterprise systems

- Testing: unit and integration test patterns for LLM-dependent code deterministic test harness design

- Observability: OTel SDK instrumentation with trace-ID propagation across agent hops

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- Qualification 15 years full time education

📌 Custom Software Engineering Lead (Karnataka)
🏢 Accenture
📍 Karnataka