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Known multinational brand and metro Bengaluru location increase applicant density, but specialized GenAI agent skills moderate competition.
Core GenAI, LLM, and RAG skills are highly transferable across industries, reducing background sensitivity.
Explicit 2–4 year requirement plus many mandatory GenAI, agent, and LLM orchestration skills makes filtering stringent.
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Own end-to-end lifecycle of Generative AI, Agentic AI, and applied AI/ML solutions, including design, prototyping, training, fine-tuning, deployment, and production inference.
Design and build advanced retrieval-augmented generation (RAG) workflows and complex multi-agent AI systems with agent interoperability protocols (A2A, ACP, MCP).
Collaborate with engineering teams for integration, deployment, optimization, and contribute to AI product innovation and evangelism within an enterprise AI platform environment.
2–4 years of applied AI/ML engineering experience with proven delivery of production Generative and Agentic AI solutions.
Hands-on expertise in training/fine-tuning language and vision models and deploying them for production inference.
Proficiency in Python and experience integrating major LLM providers (OpenAI, Azure OpenAI, Anthropic/Claude, etc.).
Familiarity or willingness to quickly ramp up on Azure AI ecosystem including Azure OpenAI and AI services.
Experienced builder capable of quickly translating AI concepts into working prototypes and production systems.
Strong technical skills across AI domains including Generative AI, multi-agent orchestration, advanced RAG pipelines, and prompt/context engineering.
Product-oriented engineer who collaborates cross-functionally and understands business impact beyond just coding.