Lead AI Engineer- LLMOps/MLOps

Were 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 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 inference

Drive LLM-specific infrastructure: memory management, token control, prompt chaining, and context optimization

Lead scalable deployment frameworks for LLMs using Kubernetes and GPU-aware scaling

Build agentic AI operations capabilities including agent evaluation, observability, orchestration and reflection loops

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

Platform 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, FastAPI

Prompt evaluation and scoring systems: OpenAI evals, Ragas, Rebuff, Outlines

Cloud-native deployment: Kubernetes, Helm, Terraform, Docker, GitOps

ML pipeline: Airflow, Prefect, Feast, Feature Store

Data stack: Spark/Flink, Parquet/Delta, Lakehouse patterns

Cloud: Azure ML, GCP Vertex AI, AWS Bedrock/SageMaker

Languages: Python (must), Bash, YAML, Terraform HCL (preferred)