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Product thinking that includes ideation, prototyping, and scale internal accelerators for LLMOps
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Architect and build scalable LLMOps platforms for enterprise-grade GenAI systems
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Design and manage end-to-end LLM pipelines from data ingestion and embedding to evaluation and inference
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Drive LLM-specific infrastructure**:** memory management, token control, prompt chaining, and context optimization
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Lead scalable deployment frameworks for LLMs using Kubernetes and GPU-aware scaling
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Build agentic AI operations capabilities including agent evaluation, observability, orchestration and reflection loops
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Guardrails & Observability: Implement output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident response
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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
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LLMOps frameworks: LangChain, MLflow, BentoML, Ray, Truss, FastAPI
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Prompt evaluation and scoring systems: OpenAI evals, Ragas, Rebuff, Outlines
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Cloud-native deployment: Kubernetes, Helm, Terraform, Docker, GitOps
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ML pipeline: Airflow, Prefect, Feast, Feature Store
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Data stack: Spark/Flink, Parquet/Delta, Lakehouse patterns
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Cloud: Azure ML, GCP Vertex AI, AWS Bedrock/SageMaker
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Languages: Python (must), Bash, YAML, Terraform HCL (preferred)