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Role overview:
We’re building a next-gen LLMOps team at Fractal to industrialize GenAI implementation and shape the future of GenAI engineering. This is a hands-on technical leadership role for AI engineers with robust ML and DevOps skills — ideal for those who love building scalable systems from the ground up. You will be designing, deploying, and scaling GenAI and Agentic AI applications with robust lifecycle automation and observability.
Required Qualifications:
10 - 14 years of experience in working on ML projects that includes product building mindset, strong hands on skills, technical leadership, leading development teams
Model development, training, deployment at scale, monitoring performance for production use cases
Strong knowledge on Python, Data Engineering, FastAPI, NLP
Knowledge on Langchain, Llamaindex, Langtrace, Langfuse, LLM evaluation, MLFlow, BentoML
Should have worked on proprietary and open-source LLMs




Experience on LLM fine tuning including PEFT/CPT
Experience in creating Agentic AI workflows using frameworks like CrewAI, Langraph, AutoGen, Symantec Kernel
Experience in performance optimization, RAG, guardrails, AI governance, prompt engineering, evaluation, and observability
Experience in GenAI application deployment on cloud and on-premises at scale for production using DevOps practices
Experience in DevOps and MLOps
Good working knowledge on Kubernetes and Terraform
Experience in minimum one cloud: AWS / GCP / Azure to deploy AI services
Team player with excellent communication and presentation skills
Must have skills:
Product thinking that includes ideation, prototyping, and scale internal accelerators for LLMOps
Architect and build scalable LLMOps platforms for enterprise-grade GenAI systems
Design and manage end-to-end LLM pipelines from data ingestion and embedding to evaluation and infer

📌 Lead Architect (Pune)
🏢 Fractal Analytics
📍 Pune