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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 strong 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 teamsModel development, training, deployment at scale, monitoring performance for production use casesStrong knowledge on Python, Data Engineering, FastAPI, NLPKnowledge on Langchain, Llamaindex, Langtrace, Langfuse, LLM evaluation, MLFlow, BentoMLShould have worked on proprietary and open-source LLMsExperience on LLM fine tuning including PEFT/CPTExperience in creating Agentic AI workflows using frameworks like CrewAI, Langraph, AutoGen, Symantec KernelExperience in performance optimization, RAG, guardrails, AI governance, prompt engineering, evaluation, and observabilityExperience in GenAI application deployment on cloud and on-premises at scale for production using DevOps practicesExperience in DevOps and MLOpsGood working knowledge on Kubernetes and TerraformExperience in minimum one cloud: AWS / GCP / Azure to deploy AI servicesTeam player with excellent communication and presentation skills

Must have skills:

Product thinking that includes ideation, prototyping, and scale internal accelerators for LLMOpsArchitect and build scalable LLMOps platforms for enterprise-grade GenAI systemsDesign and manage end-to-end LLM pipelines from data ingestion and embedding to evaluation and inferenceDrive LLM-specific infrastructure: memory management, token control, prompt chaining, and context optimizationLead scalable deployment frameworks for LLMs using Kubernetes and GPU-aware scalingBuild agentic AI operations capabilities including agent evaluation, observability, orchestration and reflection loopsGuardrails & Observability: Implement output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident responsePlatform Automation for LLMOps: Drive end-to-end automation with Docker, Kubernetes, GitOps, DevOps, Terraform, etc.

Product Thinking: Ideate, prototype, and scale internal accelerators and reusable components for LLMOps

GenAI Engineering: Productionize LLM-powered applications with modular, reusable, and secure patterns

Pipeline Architecture: Create evaluation pipelines — including prompt orchestration, feedback loops, and fine-tuning workflows

Prompt & Model Management: Design systems for versioning, AI governance, automated testing, and prompt quality scoring

Scalable Deployment: Architect cloud-native and hybrid deployment strategies for large-scale inference

Guardrails & Observability: Implement output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident response

DevOps & Platform Automation: Drive end-to-end automation with Docker, Kubernetes, GitOps, Terraform, etc.

Must-Have Technical Skills

LLMOps frameworks: LangChain, MLflow, BentoML, Ray, Truss, FastAPIPrompt evaluation and scoring systems: OpenAI evals, Ragas, Rebuff, OutlinesCloud-native deployment: Kubernetes, Helm, Terraform, Docker, GitOpsML pipeline: Airflow, Prefect, Feast, Feature StoreData stack: Spark/Flink, Parquet/Delta, Lakehouse patternsCloud: Azure ML, GCP Vertex AI, AWS Bedrock/SageMakerLanguages: Python (must), Bash, YAML, Terraform HCL (preferred)

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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