Senior AI Engineer - Agentic Systems & Data Pipelines

Collaboration.Ai • Minneapolis, MN

9d • Remote

About The Position

Collaboration.Ai is seeking a Senior AI Engineer to build the agentic systems and data pipelines behind NetworkOS's AI capabilities. This role involves developing production agent workflows using industry-leading agent SDKs and harnesses, MCP servers, and Agent Skills standards. You will also be responsible for the evaluation and observability layer to ensure measurable LLM quality, and the ingestion pipelines that transform diverse data sources into queryable knowledge. This is an execution-focused role where you will commit code weekly, ship agents as product capabilities, and contribute to a roadmap focused on graph and agents technology for clients in defense, healthcare, and regulated enterprises. The opportunity is remote, with a preference for candidates in the Twin Cities area, but all qualified candidates are encouraged to apply.

Requirements

• 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: experience with robust, scalable pipelines for AI/ML workloads.
• LLM operations experience: familiarity with evals and observability for production LLM systems (quality, cost, latency).
• Production retrieval experience: experience with 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 (due to DoD contracting and FedRAMP compliance).

Nice To Haves

• 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.

Responsibilities

• Ship production agent systems: design, build, and operate agentic workflows (agent SDKs, MCP servers, Agent Skills standards) powering AI-driven matching, analysis, and data intelligence.
• Operationalize LLM quality: build the eval and observability layer with Langfuse, golden datasets, LLM-as-judge patterns, and FinOps-style tracking to ensure measurable quality, cost, and latency for all workflows.
• Engineer data pipelines: ensure robust ingestion of documents, structured data, and external sources into searchable knowledge bases with quality validation, deduplication, and incremental updates.
• Own retrieval quality: implement and improve hybrid search combining vector, keyword, and metadata retrieval through reranking, query expansion, and contextual compression.
• Accelerate with AI: build custom MCP tools and Agent Skills to enhance the productivity of the entire engineering team.
• Execute alongside the team: pair with full-stack engineers on AI integration points, contribute to incident response for AI services, and maintain hands-on coding.

Benefits

• Real AI engineering, not a wrapper shop.
• Production agents, hybrid retrieval, continuous evals, and a roadmap heading into graph + agents with autonomy to shape how it's built.
• AI-native by default: build with AI, not just for AI.
• Agentic coding tools (Claude Code/Codex/etc.), agent SDKs and harnesses, MCP servers, and Agent Skills standards are used daily.
• Work that matters in defense, healthcare, and regulated industries.
• SOC 2 and NIST compliance, FedRAMP readiness.
• Small, senior team with early-stage impact.
• Opportunity to help set the bar for AI engineering.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed