Agentic AI Engineer

We are looking for a highly hands-on Agentic AI Engineer with 7-9 years of experience and strong expertise in Python backend engineering to build and productionize AI-powered, agentic workflows. The ideal candidate should have strong coding fundamentals, experience building scalable backend systems, and hands-on exposure to LLMs, RAG, AI Agents, orchestration frameworks, and production-grade GenAI applications.
Responsibilities:
Design, develop, and deploy Agentic AI applications and intelligent workflows using Python.
Write high-quality, scalable, maintainable Python code and contribute hands-on to architecture and implementation.
Build AI agents capable of reasoning, planning, tool calling, task execution, and multi-step workflows.
Develop and optimise RAG pipelines including document ingestion, chunking, embeddings, retrieval, reranking, and context management.
Integrate and work with LLMs such as OpenAI, Anthropic, Gemini or equivalent models.
Build agent orchestration using frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or similar.
Develop robust APIs and backend services using FastAPI, Flask, or Django.
Work with Vector Databases such as Pinecone, Weaviate, Milvus, pgvector, Chroma, or equivalent.
Implement tool/function calling, memory, planning, state management, and multi-agent workflows.
Build production-ready AI systems with appropriate evaluation, observability, monitoring, logging, and guardrails.
Collaborate closely with Product, Engineering, Data Science, and AI teams to take solutions from 01 and into production.
Optimise AI applications for latency, scalability, reliability, cost, and response quality.
Stay current with emerging developments in Agentic AI, LLMs, RAG, MCP, AI orchestration, and GenAI infrastructure.
Requirements:
7-9 years of strong software engineering experience.
Excellent hands-on Python coding skills with a strong understanding of OOP, data structures, algorithms, APIs, and software design principles.
Strong backend development experience with Python + FastAPI/Flask/Django.
Hands-on experience building LLM/GenAI applications in production.
Strong understanding of Agentic AI concepts and architectures.
Experience with RAG architecture and LLM application development.
Hands-on experience with one or more agent frameworks: LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, etc.
Experience with Vector Databases and embeddings.
Strong experience with REST APIs, microservices, distributed systems, and asynchronous processing.
Experience integrating LLM APIs and AI tools/function calling.
Strong understanding of prompt engineering, context management, hallucination mitigation, and AI evaluation.
Good to Have:
Experience with MCP (Model Context Protocol).
Experience building multi-agent systems and autonomous workflows.
Experience with LLM evaluation/observability tools such as LangSmith, Ragas, Arize, or similar.
Exposure to AWS/GCP/Azure, Docker, Kubernetes, and CI/CD.
Experience with event-driven architectures, queues, and distributed systems.
Experience taking an Agentic AI product from 01 to production scale.
Strong understanding of AI security, responsible AI, and production guardrails.
Strong Python engineer first, AI engineer second; excellent coding ability is critical.
Someone who can independently design, code, debug, optimise, and productionize complex AI systems.
Strong problem-solving and system-design capabilities.
Ability to work in a fast-paced environment and take ownership of 01 AI initiatives.
Strong communication and cross-functional collaboration skills.

Experience
7-11 yrs