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Job Description
Job Title: Python + Agentic AI Engineer
Experience: 4–8 Years
Location: Pune / Hyderabad / Chennai / Bangalore
Work Mode: Hybrid
Job Summary
We are looking for an experienced Agentic AI Engineer with 4–8 years of software development experience and strong hands-on expertise in Python, Generative AI, LLMs, RAG, and Agentic AI systems.
The ideal candidate should have experience designing and developing AI agents, agentic workflows, LLM-powered applications, and enterprise AI solutions using technologies such as LangChain, LangGraph, Vector Databases, APIs, Azure, and CI/CD.
The candidate will be responsible for building scalable and production-ready Agentic AI solutions that can reason, use tools, retrieve information, execute tasks, and interact with enterprise systems.
Mandatory Technical Skills-
Programming
- Strong hands-on experience with Python.
- Strong understanding of Object-Oriented Programming.
- Experience in Python-based application and API development.
- Strong problem-solving and debugging skills.
Generative AI & LLMs
- Hands-on experience working on Generative AI projects.
- Strong understanding of Large Language Models (LLMs).
- Experience with LLM integration and API-based AI solutions.
- Hands-on experience with Retrieval-Augmented Generation (RAG).
- Strong understanding of Prompt Engineering.
- Hands-on experience developing Agentic AI applications and workflows.
Agentic AI
- Experience designing and developing AI Agents capable of executing multi-step tasks.
- Understanding of agent orchestration and workflow automation.
- Experience with tool/function calling and integrating external tools or enterprise APIs.
- Experience building multi-step and stateful agent workflows.
- Understanding of agent memory, context management, and decision-making workflows.
- Experience with multi-agent systems is preferred.
- Ability to integrate LLMs with enterprise applications and business processes.
GenAI Frameworks
- Strong hands-on experience with LangChain.
- Strong hands-on experience with LangGraph.
- Experience building agentic workflows using LangChain/LangGraph.
- Understanding of agent orchestration, tools, nodes, states, and workflows.
RAG & Vector Databases
- Hands-on experience building RAG pipelines.
- Experience with Vector Databases.
- Understanding of Embeddings and Semantic Search.
- Experience implementing vector-based information retrieval.
- Ability to optimize retrieval pipelines for relevance and performance.
Cloud & DevOps
- Hands-on experience with Microsoft Azure.
- Experience developing and integrating REST APIs.
- Understanding of microservices architecture.
- Experience with CI/CD pipelines.
- Experience deploying and managing AI applications in cloud environments.
- Understanding of production deployment, monitoring, scalability, and reliability.
Databases
- Strong understanding of SQL.
- Experience working with databases for AI application development and data retrieval.
- Understanding of data integration with RAG and Agentic AI applications.
Key Responsibilities
- Design, develop, and deploy Agentic AI applications using Python.
- Build intelligent AI agents capable of reasoning, tool usage, information retrieval, and task execution.
- Design and implement agentic workflows using LangChain and LangGraph.
- Develop RAG-based Agentic AI solutions for enterprise use cases.
- Integrate LLMs into enterprise applications and business processes.
- Develop AI agents that interact with APIs, databases, tools, and external systems.
- Implement effective Prompt Engineering strategies to improve agent performance.
- Design and implement Vector Database solutions for semantic search and knowledge retrieval.
- Build scalable microservices and REST APIs for AI-powered applications.
- Develop and integrate tool/function-calling capabilities for AI agents.
- Implement agent memory, state management, context handling, and workflow orchestration.
- Deploy and manage Agentic AI solutions on Microsoft Azure.
- Implement CI/CD pipelines for application deployment.
- Optimize LLM, RAG, and agent workflows for performance, scalability, reliability, and cost efficiency.
- Troubleshoot and resolve issues related to LLM integrations, RAG pipelines, APIs, and agent workflows.
- Collaborate with architects, developers, data scientists, and business stakeholders to design and deliver enterprise AI solutions.
- Ensure Agentic AI applications are secure, scalable, reliable, and production-ready.
Required Experience
- 4–8 years of overall software development experience.
- Strong hands-on experience in Python development.
- Proven experience delivering Generative AI / LLM-based applications.
- Hands-on experience developing Agentic AI solutions or AI agents.
- Strong experience with RAG and LLM integration.
- Hands-on experience with LangChain and LangGraph.
- Experience with Vector Databases, embeddings, and semantic search.
- Experience developing REST APIs and microservices.
- Experience with Azure and CI/CD.
- Strong understanding of SQL and database integration.
- Ability to clearly explain AI architecture, agent workflows, technical implementation, and individual contributions.
Preferred Candidate Profile
- Strong understanding of Agentic AI architecture and LLM-based application development.
- Experience with multi-agent systems and agent orchestration.
- Good understanding of AI agent planning, tool use, memory, state management, and workflow execution.
- Strong programming and analytical skills.
- Ability to design scalable and production-ready AI solutions.
- Ability to independently troubleshoot and optimize AI applications.
- Comfortable working as an Individual Contributor and taking ownership of assigned tasks.
- Ability to clearly explain previous projects, architecture, implementation approach, and personal technical contributions.
Core Technology Stack
- Python | Generative AI | LLMs | Agentic AI | AI Agents | RAG | Lang Chain | Lang Graph | Vector Databases | Embeddings | Semantic Search | Prompt Engineering | Tool Calling | REST APIs | Microservices | Azure | CI/CD | SQL
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More Info
Key Skills
LangChain
Tool Calling
Generative AI
LLMs
AI Agents
CI CD
LangGraph
Vector Databases
Agentic AI
Embeddings
RAG
Prompt Engineering




