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Tier-1 brand, popular generative AI role, metro location, and broad skill requirements.
Core LLM and cloud engineering skills are broadly transferable, with moderate enterprise integration specifics.
Many mandatory technical skills (LLMs, RAG, vector DBs, cloud, observability) increase filter strictness.
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Design and build production-grade, enterprise-scale AI systems focused on LLM-powered applications, agentic workflows, and backend services.
Develop Retrieval-Augmented Generation (RAG) solutions and orchestrate multi-agent workflows with modern frameworks.
Integrate and deploy scalable AI solutions with backend APIs, data pipelines, vector databases, and cloud containerization ensuring observability, security, and compliance.
Strong Python and backend engineering skills with experience in frameworks like FastAPI or Flask.
Experience building and deploying LLM-based applications, RAG systems, and agentic workflows; knowledge of orchestration frameworks such as LangChain, LangGraph, or Semantic Kernel.
Experience with major cloud platforms (Azure, AWS, or GCP) and containerization tools like Docker and Kubernetes.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in building AI systems that deliver measurable business impact at enterprise scale, particularly with LLM-powered copilots and intelligent assistants.
Capable of working cross-functionally with data, platform, and product teams to enable AI-driven automation.
Demonstrates strong understanding of data engineering concepts and responsible AI principles embedded in secure, GDPR-compliant solutions.