Senior AI Engineer

Role Overview:

We are looking for a Senior AI Engineer with 4+ years of experience to lead the development of autonomous agentic systems. You will focus on building stateful AI workflows, standardizing tool connectivity via MCP, and orchestrating complex business logic using tools like LangGraph, n8n or similar no-code tools, etc.

Key Responsibilities

• Agentic Orchestration: Design and implement multi-agent systems and stateful workflows using LangChain and LangGraph .
• MCP Implementation (Must-Have): Build and maintain Model Context Protocol (MCP) servers to create a standardized, interoperable tool layer for our AI agents.
• Workflow Automation: Use n8n to architect complex, multi-app integrations and automate business processes.
• AI-Native Development: Utilize Claude Code and other agentic CLI tools to navigate, refactor, and build high-quality codebases efficiently.
• Python App Dev: Develop robust backend services and APIs ( Fast API/Flask/Django) to support AI-driven features.
• System Design: Apply core system design concepts (scaling, rate-limiting, async processing) to ensure reliable and cost-effective AI operations.

Required Skills Qualifications

• Overall 4+ years of experience in Python development with a focus on asynchronous programming and minimum 2+ years of experience in working on Agentic AI applications.
• Agent Frameworks: Deep hands-on experience with LangGraph and LangChain for building autonomous reasoning systems. Proficiency in building and managing complex workflows in no-code tools like n8n .
• Prompt Engineering : Very good prompt engineering skills Zeroshot, Fewshot, Context Engineering.
• RAG VectorDB: Crisp understanding of Retrieval-Augmented Generation (RAG) and hands-on experience with Vector Databases (e.g., Pinecone, Weaviate, or Chroma) for efficient semantic search.
• MCP Tool Calling: Proven ability to build and deploy custom servers using the Model Context Protocol.
• Modern Tooling: High proficiency with Claude Code or similar agentic development environments.
• System Design: Strong knowledge of software architecture, API design, and data modeling.
• Basics of ML/NLP: Familiarity with embeddings, prompt engineering, and core NLP concepts.

Preferred / Nice-to-Have

• MLOps: Knowledge of LLM monitoring (LangSmith), evaluation frameworks, and model versioning.
• Cloud Infrastructure: Experience deploying AI services on AWS, GCP, or Azure.
• MCP Ecosystem: Contribution to open-source MCP servers or early adoption of the protocol.
• Application Development : Frontend skillset React/Angular, etc.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.