Lead Architect

Its fun to work in a company where people truly BELIEVE in what they are doing!

Were committed to bringing passion and customer focus to the business.

Role overview:

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 forAI 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 pipelinesfrom data ingestion and embedding to evaluation and inference
• DriveLLM-specific infrastructure:memory management token control prompt chaining and context optimization
• Lead scalabledeployment frameworks for LLMsusing Kubernetes and GPU-aware scaling
• Buildagentic AIoperationscapabilitiesincluding 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 youll enjoy your career with us!

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