This role is for an Assistant Manager focusing on Creative Technology, specifically in Automation, AI, ML, and RPA. The position is based in Bengaluru and involves a hybrid work model. As a Lead AI Engineer, you will architect and implement enterprise-grade AI solutions, with a significant emphasis on Generative AI (GenAI), agent-based systems, and Large Language Model (LLM) orchestration.

Your responsibilities will include owning the technical roadmap, promoting engineering best practices, and acting as a thought leader in developing scalable, secure, and efficient AI workflows. You will be instrumental in driving innovation and enhancing the company's applied AI capabilities.

Key responsibilities involve the end-to-end technical design and delivery of GenAI/agentic systems for diverse applications. You will architect multi-agent workflows utilizing tools like LangChain and A2A protocols. Guiding the design and tuning of prompt architectures, context strategies, and hybrid RAG pipelines is crucial. Integrating AI services into enterprise platforms like Azure Foundry and Databricks is also a core part of the role.

Leading engineering teams, mentoring engineers, and ensuring technical alignment are essential. You will focus on pushing performance boundaries through research and optimization. Defining reusable templates, shared components, and internal GenAI SDKs, while enforcing standards for ethical AI use, context control, prompt security, and hallucination mitigation, will be a priority.

The ideal candidate will possess over 7 years of experience in AI/ML/GenAI solutioning, with at least 3 years in a technical leadership capacity. A deep proficiency in Python 3, including libraries such as openai, pydantic, transformers, faiss, and langchain, is required. Demonstrated experience in deploying scalable GenAI solutions with cloud-native design principles is necessary.

Strong working knowledge of Azure cloud services, GitHub workflows, and CI/CD best practices is expected. Experience in optimizing vector stores, controlling token levels, and managing prompt performance is also essential. Hands-on leadership in projects involving MCP, A2A orchestration, or custom agentic services, along with familiarity with prompt observability and compliance tooling, would be beneficial.