Role Overview :

As an AI Engineer specializing in GenAI and Multi-Agent Systems, you will be at the forefront of building autonomous, intelligent workflows that redefine how our business processes data and interacts with users. You will work closely with cross-functional teams of data scientists, product managers, and software engineers to architect, deploy, and scale sophisticated LLM-driven applications. Your day-to-day will involve moving beyond simple prompt engineering to designing complex, multi-agent frameworks that can reason, plan, and execute tasks independently. By bridging the gap between cutting-edge research and production-grade systems, you will directly influence the efficiency of our internal operations and the quality of the AI-powered experiences we deliver to our global customer base.

Key Responsibilities :

- Architect and implement multi-agent systems using frameworks like LangChain, AutoGen, or CrewAI to automate complex, multi-step business workflows for our internal stakeholders.

- Fine-tune and optimize Large Language Models (LLMs) to ensure high-accuracy, domain-specific performance that meets the rigorous quality standards of our enterprise clients.

- Develop robust RAG (Retrieval-Augmented Generation) pipelines that integrate seamlessly with our existing data infrastructure to provide context-aware, hallucination-free responses.

- Collaborate with engineering teams to establish MLOps best practices, ensuring that GenAI models are monitored, evaluated, and updated in production environments with minimal latency.

- Translate ambiguous business requirements into technical AI roadmaps, ensuring that our AI initiatives provide measurable ROI and competitive advantages for the organization.

Required Skillset :

- Demonstrated expertise in building and deploying production-grade GenAI applications using Python, PyTorch, or TensorFlow, with a deep understanding of transformer architectures.

- Proven ability to design and orchestrate multi-agent workflows, showing a clear grasp of agentic reasoning, tool-use, and memory management in AI systems.

- Strong proficiency in working with vector databases like Pinecone, Milvus, or Weaviate to support high-performance retrieval systems.

- Exceptional communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders and influence product direction.

- A collaborative mindset that thrives in a hybrid work environment in Bengaluru, balancing independent research with active participation in team-wide code reviews and architectural brainstorming.

- A solid academic foundation in Computer Science, Data Science, or a related quantitative field, typically evidenced by a degree from a top-tier institute.

- Adaptability to rapidly evolving AI research, with a track record of quickly prototyping and integrating new open-source models into existing product stacks.