Lead Architect

Fractal Analytics Ltd.; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Fractal Analytics Ltd. in Bengaluru seeks a hands-on technical leader for GenAI and LLMOps, building scalable AI systems with robust lifecycle automation and observability. You will design, deploy, and scale GenAI and Agentic AI applications, guiding a team of AI engineers who own product-grade ML pipelines.

The role emphasizes strong ML and DevOps skills, Kubernetes expertise, and the ability to deliver production-grade GenAI services on cloud and on-premises.

Qualifications

• Experience on ML projects with a product-building mindset.
• Strong hands-on skills and technical leadership, leading development teams.
• Experience in model development, training, deployment at scale, monitoring production.
• Proficient in Python, data engineering, FastAPI, NLP; knowledge of LangChain, LlamaIndex, Langtrace, Langfuse, and MLFlow.

Responsibilities

• Design, deploy, and scale GenAI and Agentic AI applications with lifecycle automation and observability.
• Provide hands-on technical leadership and guide AI engineers in delivery of scalable systems.
• Drive end-to-end LLM pipelines from data ingestion to evaluation and inference using DevOps practices.

Skills

LLMOps

GenAI

Python

Kubernetes

DevOps

Observability

Performance optimization

Tools

LangChain

MLflow

BentoML

Ray

FastAPI

Terraform

Kubernetes

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 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)

About us

At Fractal, towards our goal of “powering every human decision in the enterprise”, our partnerships and alliances help in creating and delivering a compelling suite of solutions to unlock value. We partner with companies from around the globe, leaders in their respective fields. With Fractal’s expertise in artificial intelligence, design, engineering, and digital transformation, combined with the data, technology, and software platforms from our partners, we create cutting‑edge solutions to problems in the business world. We understand how critical and timely decision triggers, and information, empower our clients to create, unlock, deliver, and realize value. Together with our partners, our goal is to serve each client in their end‑to‑end data‑to‑decision journey.