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Senior, highly specialized LLMOps role with niche skills reduces applicant density despite Bangalore location.
Highly specialized LLMOps, fine-tuning and GenAI platform skills limit transferability across industries.
Explicit 10-14 years plus extensive mandatory LLM, MLOps, DevOps, and cloud requirements drive strict filtering.
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Lead design, deployment, and scaling of GenAI and Agentic AI applications with full lifecycle automation and observability.
Architect and build scalable LLMOps platforms and end-to-end pipelines, including data ingestion, embedding, evaluation, and inference for enterprise-grade GenAI systems.
Drive infrastructure optimization, agentic AI operations, guardrails, observability, and platform automation using Kubernetes, Docker, Terraform, and DevOps practices.
10-14 years of experience in ML projects with product building and technical leadership.
Strong hands-on expertise in Python, DevOps, MLOps, FastAPI, NLP, and cloud platforms (AWS/GCP/Azure).
Experience with LLM frameworks (LangChain, MLflow, BentoML), LLM fine-tuning (PEFT/CPT), and Agentic AI workflows (CrewAI, Langraph, AutoGen).
Must have worked on proprietary and open-source LLMs with scalable production deployment experience using Kubernetes and Terraform.
Experienced technical leader capable of building and scaling LLMOps platforms and pipelines from ground zero to production.
Deep expertise in GenAI application deployment, performance optimization, prompt engineering, and AI governance within enterprise environments.
Strong strategic orientation towards platform automation, infrastructure optimization, and designing modular, reusable AI components and evaluation workflows.