Lead Architect
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Before you apply
Location5 Locations
Typefull-time
SeniorityLead
Experience10–14 yrs
DepartmentEngineering
Company size1,001–5,000 people
PostedJul 28, 2026 · 3mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
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
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
DevOps & Platform Automation: Drive end-to-end automation with Docker, Kubernetes, GitOps, Terraform, etc.
Skills
Python
FastAPI
NLP
Langchain
Llamaindex
Langtrace
Langfuse
MLFlow
BentoML
CrewAI
Langraph
AutoGen
Symantec Kernel
Kubernetes
Terraform
AWS
GCP
Azure
Docker
Helm
GitOps
Ray
Truss
OpenAI evals
Ragas
Rebuff
Outlines
Airflow
Prefect
Feast
Feature Store
Spark
Flink
Parquet
Delta
Azure ML
GCP Vertex AI
AWS Bedrock
AWS SageMaker
Bash
YAML
Terraform HCL
About the company
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.
Responsibilities
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!
Hiring Related Queries
India: [email protected]
Hiring process
Outside India: [email protected]
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