AI Native Software Engineer

Overview

Build and ship production-ready software using AI throughout the engineering lifecycle. Design, build, deploy, and continuously improve AI-enabled applications while maintaining quality and compliance.

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 with Qdrant vector search and sentence-transformer embeddings.
• Develop FastAPI backend services with async I/O, Pydantic v2 models, and JWT/RBAC security.
• Build React 18 and TypeScript frontend features including visual graph editors, real-time SSE streaming, and observability dashboards.
• Integrate A2A, MCP, and AG-UI enterprise protocols.
• Connect to ALM/PLM tools through REST and GraphQL APIs with OAuth2/SSO authentication using SAML and OIDC.
• Implement dual-tier agent memory using Redis L1 and 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 with Azure Pipelines or Jenkins.
• Serve and manage on-prem LLM models through vLLM alongside cloud-hosted models using OpenAI-compatible APIs.
• Collaborate with various teams to innovate and improve the software lifecycle.

What you'll need

• Digital Full Stack Development skills.
• Minimum 3 year(s) of experience in 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.
• Strong async Python and modern React/TypeScript skills.
• Experience with graph databases or knowledge graph systems.
• Comfort owning features across the full stack, from API to UI to deployment.
• Python 3.10+ with production-grade FastAPI, Pydantic v2, and async/await patterns.
• LangGraph or LangChain, including state machines, tool calling, and agent orchestration.
• LLM engineering, including prompt design, token budgeting, multi-model routing, and self-healing retry loops.
• PostgreSQL with JSONB and asyncpg or MongoDB with Motor for async data persistence.
• Neo4j, including Cypher queries, entity-relationship graph modeling, and traversal algorithm.
• Vector databases such as Qdrant, Pinecone, or Weaviate, including embedding pipelines, semantic search, and hybrid retrieval.
• RAG architecture, including chunking strategies, re-ranking, and token budget management.
• Sentence-transformers or equivalent embedding models.
• React 18, TypeScript, and Vite, including component architecture, hooks, and lazy loading.
• Server-Sent Events or WebSocket for real-time streaming UIs.
• ReactFlow or XYFlow for interactive node-based graph editors.
• State management with Zustand or Redux and server-state management with TanStack Query.
• Python programming language.
• TypeScript.
• Software development.

Nice to have

• Python programming language.
• TypeScript.
• Software development.

Details

• Based at the Bengaluru office.