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.

Skills

GenAI, LLM, Large Language Models, Python, RAG, Retrieval-Augmented Generation