Senior ML / AI Operations Engineer

The role

You will architect, deploy, and scale a core AI compute platform. This role involves orchestrating distributed GPU/TPU training clusters, high-throughput inference serving pipelines, and agentic execution environments for energy-domain models.

What you will do

• Architect secure, scalable, and isolated runtime environments for autonomous AI agents.
• Build orchestrations for multi-step agent workflows with state management and human-in-the-loop capabilities.
• Implement deterministic safety guardrails and policy verification for agent actions.
• Maintain and optimize scalable distributed training pipelines.
• Design, deploy, and operate high-throughput, low-latency model serving clusters.
• Optimize resource allocation, job scheduling, and fault tolerance across hardware fleets.
• Manage model lifecycle management including registry, versioning, and canary deployments.
• Implement end-to-end LLMOps infrastructure for fine-tuning and alignment.
• Build high-performance Retrieval-Augmented Generation (RAG) platforms.
• Deploy ML and Agent Ops observability stacks to trace hardware metrics and model performance.

Required qualifications

• 5+ years of software engineering or DevOps experience
• 3+ years dedicated to MLOps, LLMOps, or AI platform infrastructure operating at scale
• Technical mastery of Docker, Kubernetes, Helm, and custom Kubernetes Operators
• Experience with enterprise cloud ecosystems and Infrastructure as Code (Terraform)
• Hands-on experience with PyTorch, Ray, vLLM, Hugging Face DeepSpeed, and Triton Inference Server
• Advanced proficiency in Python
• Familiarity with distributed systems architecture
• Track record of driving architectural decisions, mentoring engineers, and collaborating with AI researchers and product teams

Nice to have

• Experience with machine learning applied to the energy domain, especially dealing with datasets spanning imagery, time series, grid models and text
• Direct experience deploying edge AI models to constrained industrial hardware using TensorRT, ONNX Runtime, or OpenVINO
• Track record of contributions to open-source MLOps or LLMOps frameworks (Ray, vLLM, Kubeflow, LangChain, LlamaIndex)
• Familiarity with agent orchestration frameworks (e.g., LangGraph, AutoGen, LlamaIndex, Semantic Kernel) and LLM evaluation tools
• C++ is a strong plus

Benefits

• Competitive salary and equity
• Medical, dental, and vision coverage
• Generous PTO
• Flexible hybrid work model
• 401(k) with employer contribution
• Professional development

About the company

tapestryenergy builds an intelligent platform that powers renewable energy interconnection, autonomous grid dispatch and optimization, long-term grid planning, and energy-domain foundation models.