Member of Technical Staff, Agentic AI Architect - United States

Originally posted 8 October 2026 by the employer.

About the role

This role involves architecting autonomous AI Agents and the agentic and data pipelines that power them, building robust CI/CD deployment paths, and pushing the limits of agent performance profiling and evaluation.

What you'll do

• Design and develop autonomous AI Agents capable of multi-step reasoning, planning, and tool execution to automate complex manufacturing workflows.
• Implement Agentic frameworks to orchestrate LLM interactions with internal APIs, databases, and software tools.
• Build and maintain agent evaluation harnesses to benchmark agent accuracy, reliability, and task success across complex, multi-step workflows.
• Analyze and profile agent and inference workloads to identify and resolve bottlenecks in compute and latency, using agent observability and profiling tools (e.g., PyTorch Profiler, LangSmith, OpenTelemetry).
• Implement interoperable multi-agent systems using the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP).
• Create/maintain CI/CD pipelines for deploying AI Agents to Kubernetes and containerized environments in the cloud.
• Design and implement security controls for autonomous AI Agents, including sandboxing, access control, and mitigation of prompt-injection and tool-misuse risks.
• Mentor engineers on agentic system design paradigms and optimization techniques.

What you'll need

• Technical Degree (Bachelor’s or Master’s in Computer Science, Statistics, or a related field).
• Experience optimizing inference engines (vLLM, TensorRT-LLM) for autonomous AI Agent and LLM workloads.
• Experience developing GenAI applications and AI Agents using one or more frameworks (Microsoft Agent Framework or Google Agent Development Kit preferred).
• Proficiency with Large Language Models (LLMs), including prompt engineering, function calling/tool use, and Chain-of-Thought (CoT) reasoning.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings, and vector search.
• Experience building and operating end-to-end systems that automate the testing, evaluation, and deployment of autonomous AI Agents.
• Familiarity with MCP (Model Context Protocol) tools, plugins, and skills, and the Agent-to-Agent (A2A) protocol for building interoperable multi-agent systems.
• Understanding of AI Agent security practices, including sandboxing, access control, and mitigating prompt-injection and tool-misuse risks.
• Familiarity with machine learning frameworks (PyTorch, TensorFlow, scikit-learn).
• Strong scripting and programming skills, with software development skills and a desire to work in a cloud environment.
• 8+ years of relevant industry experience, spanning large-scale data/ML pipelines and/or production Agentic AI systems.
• Experience with Kubernetes for orchestrating containerized applications, including deployment and scaling of AI Agent workloads.

Nice to have

• Experience designing and coordinating Multi-Agent Systems, including collaboration between specialized agents.
• Experience designing and operating agent swarms for distributed, parallelized task execution.
• Experience with agent profiling and evaluation tooling to identify performance regressions and bottlenecks across agent pipelines.
• Hands-on experience with LangChain, LangGraph, or CrewAI.
• Proficiency in TypeScript and/or Rust.
• Knowledge of computer vision and/or signal processing, including techniques for classification and feature extraction.

Skills: Agentic AI, LLMs, Kubernetes, CI/CD, RAG pipelines, Python