Back
Popular AI title, mid-level experience, and Bengaluru metro location drive high applicant density.
Core ML/LLM skills transfer across industries, though agentic/healthcare platform specifics increase specialization.
Explicit 2–4 years plus mandatory generative/agentic AI, RAG, and specific LLM/tool experience increases filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end lifecycle of Generative AI, Agentic AI, and applied AI/ML solutions including design, prototyping, training, fine-tuning, and production deployment.
Build and optimize AI-powered features such as advanced RAG workflows, multi-agent systems, and reusable Python APIs for chatbots, document Q&A, summarization, and automation.
Collaborate with engineering teams to integrate AI solutions ensuring performance, cost-efficiency, security, and internal product innovation through PoCs and demos.
2–4 years of experience in applied AI/ML engineering with delivery of production Generative and Agentic AI solutions.
Strong Python proficiency including API development, async programming, and rapid prototyping.
Experience with AI model training, fine-tuning, multi-agent systems, advanced RAG pipelines, and LLM orchestration frameworks (e.g., LangChain, LlamaIndex).
Working familiarity or readiness to work on Azure AI ecosystem; primary work location Bengaluru - Technology Campus.
Experienced in building complex multi-agent AI systems with reusable agent skills and interoperability protocols (A2A, ACP, MCP).
Capable of integrating across diverse AI tooling and cloud environments with product-oriented mindset balancing technical excellence and business impact.
Comfortable collaborating cross-functionally with Full Stack, DevOps, and Cloud engineers for seamless AI solution deployment and innovation.