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Popular AI Engineer title, mid-level range, metro location, and broad GenAI skillset drive high competition.
Specialized Generative and Agentic AI skills and model fine-tuning make background fit highly sensitive.
Multiple mandatory GenAI, agent, RAG, and fine-tuning skills plus explicit 2–4 years requirement increases strictness.
Own end-to-end lifecycle of Generative AI, Agentic AI, and applied AI/ML solutions including ideation, prototyping, training, fine-tuning, inference, and production deployment.
Design, develop, and deploy large-document advanced RAG workflows, multi-agent AI systems, and reusable APIs for AI-powered features like chatbots and document Q&A.
Collaborate with engineering teams to integrate AI solutions, optimize inference pipelines, and lead AI innovation and product roadmap contributions.
2–4 years of applied AI/ML engineering experience with production Generative and Agentic AI solutions.
Strong Python proficiency including API development, async programming, and rapid prototyping.
Experience building complex multi-agent systems, advanced RAG pipelines, and LLM orchestration with providers like OpenAI or Azure OpenAI.
Working familiarity or willingness to quickly learn Azure AI ecosystem; Location: Bengaluru Technology Campus.
Demonstrates a full-spectrum AI engineering capability including model training/fine-tuning when off-the-shelf models are insufficient.
Experienced in architecting scalable AI workflows with strong product intuition to deliver measurable business impact.
Can operate collaboratively across engineering, product, and cloud teams to integrate and optimize AI solutions in enterprise environments.