Apply

Ready to go for it?

AI Apply speeds things up—apply directly if you prefer.

FREE ACCESS
5,000–10,000 jobs/day
JobTailor Logo

See all jobs on JobTailor

Search thousands of fresh jobs every day.

Discover
  • Fresh listings
  • Fast filters
  • No subscription required
Create a free account and start exploring right away.
Fractal

Lead Architect

Fractal

Technical leadership role in next-gen LLMOps team at Fractal focused on GenAI implementation and engineering. Involves designing, deploying, and scaling applications with lifecycle automation.

Posted 7/28/2026full-timeBengaluru • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in ML project development, including model training, deployment, and performance monitoring. Proficient in designing and scaling GenAI applications with a strong focus on DevOps practices and lifecycle automation.

Highest-signal resume keywords
ML Project DevelopmentGenAI Application DeploymentPython ProgrammingDevOps PracticesKubernetes

ATS Keywords

Tailor your resume
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills
Model DevelopmentTrainingDeploymentPerformance MonitoringNLPLLM Fine TuningPerformance OptimizationPrompt EngineeringAI GovernanceLifecycle Automation
Soft Skills
Technical LeadershipTeam PlayerCommunication SkillsPresentation Skills
Tools & Technologies
FastAPIMLFlowBentoMLCrewAILangchainLlamaindexLangtraceLangfuseTerraformKubernetes
Industry Keywords
GenAIAgentic AIMLOpsCloud DeploymentOpen-Source LLMs

Tech Stack

Tools & technologies
AWSAzureCloudGoogle Cloud PlatformKubernetesPythonTerraform

About the role

Key responsibilities & impact
  • 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.
  • You will be designing, deploying, and scaling GenAI and Agentic AI applications with robust lifecycle automation and observability.

Requirements

What you’ll need
  • 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.

Benefits

Comp & perks
  • health insurance
  • professional development