Role Overview We are looking for a Senior AI Engineer to lead the development of enterprise-grade, autonomous agentic systems. In this role, you will build stateful AI workflows, standardize external tool connectivity using the Model Context Protocol (MCP) , and orchestrate complex business logic using frameworks like LangGraph combined with workflow platforms like n8n .

The ideal candidate combines strong Python backend fundamentals with deep AI expertise. You are someone who already uses next-generation AI-assisted developer tooling (such as Claude Code ) to ship clean, production-ready code with high velocity.

Key Responsibilities

- Agentic Orchestration & Graph Workflows: Design, build, and deploy multi-agent systems and stateful, cyclic reasoning workflows using LangGraph and LangChain (handling memory, subgraphs, recursion limits, and human-in-the-loop interrupts).
- Tool Layer Standardization (MCP): Architect, deploy, and maintain custom Model Context Protocol (MCP) servers and client interfaces to give agents safe, standardized access to internal databases, APIs, and enterprise systems.
- Hybrid Automation Architecture: Bridge custom Python microservices with workflow automation engines like n8n to automate complex end-to-end multi-app processes and webhook-driven event pipelines.
- Production API & Systems Development: Develop robust, asynchronous microservices and endpoints (FastAPI) incorporating rate limiting, streaming responses (SSE/WebSocket), retries, and token-cost tracking.
- AI-Native Engineering: Champion AI-assisted development practices by heavily utilizing Claude Code and other agentic developer CLI tools to rapidly scaffold, test, and refactor code.
- Agent Reliability & Guardrails: Implement safety guardrails, schema validation, structured output enforcement, and automated agent evaluation suites (evaluating tool-call accuracy, task completion, and hallucination rates).

Required Skills & Qualifications

- Core Engineering Experience:



4+ years of professional backend software engineering in Python, with strong proficiency in asynchronous programming (asyncio, httpx), OOP, and clean architecture.
- Agentic Frameworks: 2+ years of hands-on experience building autonomous agents and stateful graphs using LangGraph (managing checkpointers, state schemas, and branch conditions) and LangChain.
- Model Context Protocol (MCP): Hands-on experience building, debugging, and deploying custom MCP servers (Stdio/SSE transports) and connecting them to agent runtimes.
- Contemporary Agentic Tooling: Daily fluency with Claude Code or comparable agentic CLI tools for automated testing, debugging, and rapid development.
- Structured Outputs & Tool Calling: Deep understanding of function/tool calling mechanics and schema enforcement libraries (e.g., Pydantic, Instructor, JSON Schema).
- RAG & Search: Solid foundation in Retrieval-Augmented Generation (RAG) architectures: hybrid search (dense embeddings + sparse keyword search), re-ranking, and production vector stores (e.g., Qdrant, Weaviate, Pinecone, or pgvector).
- System Design & Resilience: Strong system design fundamentals: API authentication, asynchronous task queues (Celery/Redis), rate-limiting, and error-recovery patterns for non-deterministic systems.

Preferred / Nice-to-Have

- In-House LLM Serving: Experience integrating with or deploying local/open-source models using engines like vLLM, Ollama, or Triton Inference Server on internal GPU infrastructure.
- Observability & LLMOps: Experience setting up end-to-end agent tracing, cost analysis, and evaluation frameworks using LangSmith, Arize Phoenix, or OpenTelemetry.
- Cloud Platforms: Experience running and containerizing AI services on AWS, Azure, or GCP using Docker and Kubernetes.
- MCP Open Source Ecosystem: Active contributor to or early adopter of the open-source MCP community and servers.
- Frontend Basics: Working familiarity with frontend frameworks (React, Next.js, or Tailwind) to quickly prototype internal agent-interaction UIs.

📌 Senior AI Engineer (Hyderabad)
🏢 Paltech
📍 Hyderabad