Lead Architect

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)
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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