Role &

Responsibilities -

- Design, develop, and deploy Agentic AI and multi-agent solutions for enterprise business workflows.

- Build agentic workflows using LangGraph, LangChain, Semantic Kernel, AutoGen, or equivalent frameworks.

- Develop multi-agent architectures involving supervisor, planner, router, specialist, validation, and tool-execution agents.

- Implement tool calling, function calling, API integrations, MCP, and enterprise system integrations to enable agents to perform real-world tasks.

- Build stateful, multi-step workflows involving reasoning, decision-making, API/tool execution, validation, approvals, retries, and exception handling.

- Develop RAG pipelines and integrate enterprise knowledge retrieval into agent workflows using Azure AI Search or equivalent vector/search technologies.

- Build GenAI applications using LLMs, Python, Azure OpenAI, LangChain/LangGraph and related technologies.

- Design mechanisms for agent memory, context management, prompt orchestration, structured outputs, and hallucination control.

- Integrate agents with enterprise applications, databases, APIs, ticketing systems, cloud services, and business workflows.

- Develop production-ready backend services using Python/FastAPI and support containerized/cloud deployments.

- Implement testing, evaluation, monitoring, logging, tracing, and performance optimization for LLM and agentic applications.

- Collaborate with business and engineering teams to translate complex business processes into AI-driven autonomous workflows.

- Continuously evaluate emerging agentic AI frameworks, models, tools, and orchestration patterns and apply them to enterprise use cases.

Preferred candidate profile





- 510 years of overall software/AI engineering experience, with strong recent hands-on experience in Generative AI and Agentic AI.

- Strong hands-on experience building multi-agent systems and agentic workflows, not just chatbot or basic RAG applications.

- Strong proficiency in Python and experience developing production-grade AI/backend applications.

- Hands-on experience with LangGraph and/or equivalent agent orchestration frameworks such as LangChain, Semantic Kernel, AutoGen, CrewAI, etc.

- Strong understanding of agent orchestration, state management, tool calling, function calling, workflow routing, planning/reasoning, and multi-agent collaboration.

- Experience integrating agents with REST APIs, databases, enterprise applications, cloud services, MCP or other external tools.

- Robust hands-on RAG experience, including document ingestion, chunking, embeddings, vector/semantic search, retrieval and grounding.

- Experience with Azure OpenAI, Azure AI Search or equivalent cloud GenAI services is preferred.

- Experience building production-grade GenAI/Agentic AI applications, rather than only POCs, demos, or academic projects.

- Knowledge of LLM evaluation, observability, tracing, latency/token monitoring, hallucination detection and response-quality monitoring is preferred.

- Experience with FastAPI, Docker, CI/CD and cloud deployment is an advantage.

- Strong understanding of enterprise concerns such as security, authorization, data privacy, human-in-the-loop approvals, error handling and scalability.

- Candidates should be able to clearly explain the architecture, workflow, tools, APIs and code they personally implemented in their Agentic AI projects.

📌 Artificial Intelligence Engineer (Bengaluru)
🏢 Allegis Group
📍 Bengaluru