Senior AI Engineer - Agentic Systems & Data Pipelines

Role Description

You'll build the agentic systems and data pipelines behind NetworkOS's AI capabilities:

• Production agent workflows built on industry-leading agent SDKs and harnesses, MCP servers, and Agent Skills standards.
• The eval and observability layer that keeps LLM quality measurable.
• The ingestion pipelines that turn messy, diverse data sources into queryable knowledge.

This is an execution seat, not an ivory tower. You'll commit code every week, ship agents as product capability rather than demos, and help shape a roadmap that's heading deep into graph + agents territory — for customers in defense, healthcare, and regulated enterprise.

• Agents in production.
• Pipelines that hold.
• Evals that keep everyone honest.

This opportunity is remote with a preference for candidates in the Twin Cities area (Minneapolis, Saint Paul); however, all candidates are encouraged to apply!

Qualifications

• 7+ years of professional software engineering experience, with 3+ years focused on AI/ML or data engineering.
• Production agentic/LLM application experience — built and operated systems around LLM APIs (Anthropic, OpenAI) serving real users: agents, tool-use, or orchestrated LLM workflows.
• Data engineering background — robust, scalable pipelines for AI/ML workloads.
• LLM operations experience — evals and observability for production LLM systems (quality, cost, latency).
• Production retrieval experience — vector databases and/or search engines (OpenSearch, Elasticsearch).
• Modern Python stack proficiency — FastAPI, Pydantic, async/await, modern dependency management.
• AI-native workflows — demonstrated ability to leverage Claude Code/Codex or similar agentic coding tools to accelerate development.
• Experience with Docker, Kubernetes, and AWS.
• US citizenship required (DoD contracting — IL4/IL5 environments — and FedRAMP compliance).

Requirements

• Deep agentic ecosystem experience — Agent Skills standards, custom MCP servers, agent SDKs across major vendors.
• Advanced RAG expertise — GraphRAG, agentic RAG, contextual retrieval, reranking strategies.
• Graph data experience — knowledge graphs, graph databases, or graph-based retrieval.
• Model selection & rightsizing — matching models to domain-specific use cases across quality, cost, and latency tradeoffs.
• Streaming data experience (Kafka, Kinesis) for real-time knowledge base updates.
• Research background, open-source contributions, or an advanced degree in ML/IR/NLP.

Benefits

• Real AI engineering, not a wrapper shop.
• Production agents, hybrid retrieval, continuous evals, and a roadmap heading into graph + agents — with the autonomy to shape how it's built.
• AI-native by default.
• Work that matters — Defense, healthcare, and regulated industries — SOC 2 and NIST compliance, FedRAMP readiness, and customers whose missions demand AI they can trust.
• Small, senior team — Early-stage impact with your work visible from week one. You'll help set the bar for how AI engineering is done here.