EPAM Bangalore F2F Hiring Drive SR/Lead AI Engineers on Jul 18

AI Engineer Agentic & Generative AI Solutions Role Overview We are seeking an experienced AI Engineer to design and build production-grade Generative AI solutions with a strong focus on agentic workflows, multi-agent systems, and enterprise AI applications. The ideal candidate combines strong software engineering fundamentals with hands-on expertise in LLMs, RAG architectures, and AI agent frameworks. Key Responsibilities Design, architect, and develop scalable Generative AI applications and agentic solutions for real-world business use cases. Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot Studio, or similar technologies. Develop and maintain backend services, APIs, microservices, and data pipelines that power AI-driven products. Implement advanced AI patterns including RAG, Agentic RAG, tool/function calling, planning & reflection loops, and human-in-the-loop workflows. Engineer and optimize prompts, system instructions, and agent workflows to improve reliability, accuracy, and user experience. Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowledge repositories. Monitor, evaluate, and continuously improve model and agent performance using observability tools, metrics, and user feedback. Collaborate closely with Product, Engineering, Data, and Design teams to deliver impactful AI solutions. Stay current with emerging AI technologies, frameworks, and best practices, contributing innovative ideas to the team. Document architectures, design decisions, and reusable solution patterns while supporting knowledge sharing across teams. Required Qualifications Must Have 512 years of overall software engineering experience. 3+ years of hands-on experience building applications using Generative AI and LLM technologies. Strong proficiency in Python and experience developing production-ready applications. Hands-on experience with at least two agentic AI frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, or Microsoft Copilot extensibility. Strong backend development skills, including REST/gRPC APIs, asynchronous programming, Docker, and frameworks such as FastAPI or Flask. Solid understanding of leading LLMs including OpenAI GPT models, Anthropic Claude, Google Gemini, and open-source alternatives. Practical experience building RAG solutions using vector databases such as Pinecone, Weaviate, ChromaDB, or Qdrant. Expertise in prompt engineering, LLM orchestration, structured outputs, guardrails, ReAct patterns, and evaluation techniques. Strong problem-solving, system design, and architectural decision-making skills. Excellent communication skills with the ability to collaborate effectively across global teams. Preferred Qualifications Experience with AI observability and evaluation tools such as LangSmith, Ragas, DeepEval, Arize, or Weights & Biases. Familiarity with model adaptation and fine-tuning techniques (LoRA, PEFT, RLHF concepts). Experience with cloud AI platforms including Azure OpenAI, AWS Bedrock, or Google Vertex AI. Understanding of CI/CD, MLOps, and LLMOps practices. Exposure to graph databases, knowledge graphs, and structured data integration. Experience with event-driven architectures and messaging platforms such as Kafka or RabbitMQ. What We're Looking For Engineers who build and deliver production-ready AI systems, not just prototypes. Professionals who can evaluate and recommend the right architecture, whether single-agent, multi-agent, RAG-based, or fine-tuned solutions. Strong ownership mindset with the ability to take AI solutions from concept through production deployment. Team players who contribute technical leadership, best practices, and continuous learning within a rapidly evolving AI landscape. AI Engineer Agentic & Generative AI Solutions Role Overview We are seeking an experienced AI Engineer to design and build production-grade Generative AI solutions with a strong focus on agentic workflows, multi-agent systems, and enterprise AI applications. The ideal candidate combines strong software engineering fundamentals with hands-on expertise in LLMs, RAG architectures, and AI agent frameworks. Key Responsibilities Design, architect, and develop scalable Generative AI applications and agentic solutions for real-world business use cases. Build and orchestrate AI agents using frameworks such as LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot Studio, or similar technologies. Develop and maintain backend services, APIs, microservices, and data pipelines that power AI-driven products. Implement advanced AI patterns including RAG, Agentic RAG, tool/function calling, planning & reflection loops, and human-in-the-loop workflows. Engineer and optimize prompts, system instructions, and agent workflows to improve reliability, accuracy, and user experience. Integrate LLMs with enterprise systems, third-party APIs, vector databases, and knowle