Lead Architect

Fractal Analytics; Gurugram District; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

Fractal Analytics is building a next-gen LLMOps team in Gurugram to industrialize GenAI and enable scalable AI engineering. You will lead hands-on ML/DevOps initiatives, architecting end‑to‑end GenAI pipelines and agentic AI workflows at scale.

The role demands strong Python, ML, and DevOps skills, plus experience with LangChain, LlamaIndex, and cloud deployments. Join a fast-growing team delivering production-grade GenAI solutions.

Qualifications

• Proven leadership in ML projects and product mindset.
• Hands-on development, training, and deployment at scale.
• Experience with LLMs, PEFT, and agentic AI workflows.

Responsibilities

• Design, deploy, and scale GenAI and Agentic AI apps with lifecycle automation.
• Lead ML/AI engineering teams and ensure observability and governance.
• Develop scalable LLM pipelines from data ingestion to inference.

Skills

LLMOps

Python

DevOps

Kubernetes

Terraform

LangChain

NLP

ML Engineering

Team leadership

Cloud deployment

Prompt engineering

Education

Bachelor's or Master's in CS/ML/Engineering

Tools

LangChain

MLflow

BentoML

Ray

FastAPI

Docker

Kubernetes

Terraform

GitOps

Airflow

Prefect

Feast

Job description

It’s fun to work in a company where people truly BELIEVE in what they are doing!

We’re committed to bringing passion and customer focus to the business.

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 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 manageend-to‑end LLM pipelines from data ingestion and embedding to evaluation and inference
• DriveLLM‑specific infrastructure : memory management, token control, prompt chaining, and context optimization
• Lead scalabledeployment frameworks for LLMs using Kubernetes and GPU‑aware scaling
• Buildagentic AIoperationscapabilities 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)

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