AI Native Software Engineer

Overview

Build and ship production-ready software with AI as a core part of the engineering lifecycle, applying AI-native practices to develop, validate and continuously improve solutions.

What you'll do

• Design and implement multi-agent orchestration pipelines using LangGraph state machines with conditional routing, self-healing loops, and sub-agent hierarchies
• Build and maintain a hybrid retrieval system combining Neo4j knowledge graphs (Cypher) with Qdrant vector search and sentence-transformer embeddings
• Develop FastAPI backend services with async I/O (asyncpg, Motor), Pydantic v2 models, and JWT/RBAC security
• Build React 18 / TypeScript frontend features including visual graph editors (ReactFlow), real-time SSE streaming, and observability dashboards (Recharts)
• Integrate enterprise protocols: A2A (JSON-RPC), MCP (Model Context Protocol), AG-UI (Server-Sent Events)
• Connect to ALM/PLM tools via REST and GraphQL APIs with OAuth2/SSO authentication (SAML, OIDC)
• Implement dual-tier agent memory (Redis L1 + mem0 L2) with semantic recall and context consolidation
• Build DAG-based workflow compilation with topological sorting, cycle detection, and Ray-based parallel execution
• Deploy and operate containerized services using Docker, Azure Bicep IaC, and CI/CD pipelines (Azure Pipelines / Jenkins)
• Serve and manage on-prem LLM models via vLLM alongside cloud-hosted models (OpenAI-compatible APIs)

What you'll need

• Minimum 3 years of experience in Digital Full Stack Development
• Must have skill: Digital Full Stack Development
• 15 years full time education
• 4+ years professional software engineering experience
• 2+ years working with LLMs, agent frameworks, or ML systems in production
• Python 3.10+ with production-grade FastAPI, Pydantic v2, and async/await patterns
• LangGraph / LangChain: state machines, tool calling, agent orchestration
• LLM engineering: prompt design, token budgeting, multi-model routing, self-healing retry loops
• PostgreSQL (JSONB, asyncpg) or MongoDB (Motor) for async data persistence
• Neo4j: Cypher queries, entity-relationship graph modeling, traversal algorithms
• Vector databases (Qdrant, Pinecone, or Weaviate): embedding pipelines, semantic search, hybrid retrieval
• RAG architecture: chunking strategies, re-ranking, token budget management
• Sentence-transformers or equivalent embedding models
• React 18 + TypeScript + Vite: component architecture, hooks, lazy loading
• Server-Sent Events (SSE) or WebSocket for real-time streaming UIs
• ReactFlow / XYFlow for interactive node-based graph editors
• State management (Zustand or Redux) and server-state (TanStack Query)
• Experience with graph databases or knowledge graph systems
• Comfortable owning features across the full stack, from API to UI to deployment

Nice to have

• TypeScript, Python, and Software Development skills
• vLLM or TGI for on-prem LLM model serving and optimization
• mem0 or similar persistent agent memory systems
• A2A / MCP / AG-UI agentic interoperability protocols
• GraphQL schema design and client integration
• OAuth2 / SAML / OIDC enterprise SSO integration
• Ontology modeling: entity taxonomies and relationship schemas for engineering domains
• Pytest with async test patterns and fixture-based testing

Details

• Based at the Bengaluru office