6.0 - 10.0 years

0 Lacs

hyderabad, telangana

On-site

You should have 6-10+ years of experience in AI/ML development, including at least 3 years of hands-on experience in Generative AI, RAG frameworks, and Agentic AI systems. As a Generative AI Engineer, you will be responsible for designing, developing, and optimizing RAG pipelines using frameworks such as LangChain, LlamaIndex, or custom-built stacks. You will also implement Agentic AI architectures involving task-based agents, stateful memory, planning-execution workflows, and tool augmentation. Additionally, you will perform model fine-tuning, embedding generation, and evaluation of LLM outputs while incorporating human and automated feedback loops. Building and enforcing guardrails to ensure safe, compliant, and robust model behavior will be a crucial part of your role, including prompt validation, output moderation, and access controls. Collaboration with cross-functional teams to deploy solutions in cloud-native environments such as Azure OpenAI, AWS Bedrock, or Google Vertex AI will also be expected. Furthermore, you will contribute to system observability via dashboards and logging, as well as support post-deployment model monitoring and optimization. To excel in this role, you must have proven production experience with RAG frameworks like LangChain, LlamaIndex, or custom-built solutions. A solid understanding of Agentic AI design patterns, strong expertise in LLM fine-tuning, vector embeddings, evaluation strategies, and feedback integration are essential. Experience with implementing AI guardrails, proficiency in Python, LLM APIs (OpenAI, Anthropic, Cohere, etc.), and vector database integration are also required. Familiarity with CI/CD pipelines, API integrations, and cloud-native deployment patterns is a plus. Preferred qualifications include experience working on AI projects in regulated environments (Banking domain), hands-on experience with cloud AI platforms like Azure OpenAI, AWS Bedrock, or Google Vertex AI, knowledge of prompt engineering, RLHF, and LLM observability frameworks, as well as experience building or leveraging internal LLM evaluation harnesses, agent orchestration layers, or compliance dashboards.,

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