Sr. GenAI/ Agentic AI Engineer

We are looking for a highly skilled GenAI / Agentic AI Engineer with strong expertise in LLMs, RAG, and Prompt Engineering, along with solid experience across the Software Development Lifecycle (SDLC). The ideal candidate will have hands-on experience building production-grade AI systems, integrating them into microservices architectures, and deploying them using modern DevOps practices.

Key Responsibilities

• Design, develop, and deploy scalable GenAI applications using Python, LLMs, and modern AI frameworks.
• Build end-to-end Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.
• Develop and optimize prompt engineering strategies for high-quality, context-aware AI responses.
• Implement agentic AI systems using LangChain, LangGraph, AutoGPT, or similar frameworks.
• Design autonomous AI agents capable of multi-step reasoning, planning, and tool usage.
• Integrate GenAI capabilities into microservices-based architectures using RESTful APIs and API Gateways.
• Collaborate with cross-functional teams to translate business requirements into AI-driven solutions.
• Follow SDLC best practices including requirement analysis, design, development, testing, deployment, and maintenance.
• Implement CI/CD pipelines and automate model deployment using tools like Jenkins, GitHub Actions, and Docker.
• Ensure high code quality through unit testing (Pytest), code reviews, and static analysis tools.
• Monitor, troubleshoot, and optimize performance of GenAI applications in production environments.
• Work closely with DevOps teams to manage deployment, scaling, and versioning of AI models.
• Stay updated with advancements in LLMs, GenAI, and Agentic AI technologies.

Required Skills & Qualifications

• Strong programming experience in Python with libraries such as Transformers, LangChain, and OpenAI APIs.
• Hands-on experience with RAG pipelines, embeddings, and vector databases (e.g., FAISS, Pinecone, Weaviate).
• Expertise in Prompt Engineering and LLM optimization techniques.
• Experience building Agentic AI systems with planning, memory, and tool integration.
• Solid understanding of AI/ML concepts (classification, regression, deep learning, reinforcement learning).
• Experience with SDLC tools: Git, Jira, GitHub/Bitbucket, Jenkins, SonarQube.
• Knowledge of microservices architecture, REST APIs, and API design (Swagger/OpenAPI).
• Familiarity with DevOps practices including CI/CD, Docker, Kubernetes, and Infrastructure as Code.
• Experience with testing frameworks (Pytest) and model validation techniques.
• Strong debugging, performance tuning, and problem-solving skills.