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About the Role
You will build the agentic systems and data pipelines behind NetworkOS's AI capabilities, including production agent workflows, MCP servers, and Agent Skills standards. You will also develop the eval and observability layer for LLM quality and ingestion pipelines to transform diverse data sources into queryable knowledge. This is an execution-focused role where you will commit code weekly, ship agents as product features, and contribute to a roadmap centered on graph and agents for defense, healthcare, and regulated enterprise customers.
Responsibilities
- Ship production agent systems by designing, building, and operating agentic workflows (agent SDKs, MCP servers, Agent Skills standards) for AI-driven matching, analysis, and data intelligence.
- Operationalize LLM quality by building the eval and observability layer using Langfuse, golden datasets, LLM-as-judge patterns, and FinOps-style tracking.
- Engineer robust data pipelines for ingesting documents, structured data, and external sources into searchable knowledge bases with quality validation, deduplication, and incremental updates.
- Own retrieval quality through hybrid search combining vector, keyword, and metadata retrieval, continuously improved via reranking, query expansion, and contextual compression.
- Accelerate with AI by building custom MCP tools and Agent Skills to enhance engineering team efficiency.
- Execute alongside the team by pairing with full-stack engineers on AI integration points, contributing to incident response for AI services, and actively coding.
Requirements
- 7+ years of professional software engineering experience, with 3+ years focused on AI/ML or data engineering.
- Production agentic/LLM application experience, including building and operating systems around LLM APIs (Anthropic, OpenAI) for real users.
- Data engineering background with experience in robust, scalable pipelines for AI/ML workloads.
- LLM operations experience, including evals and observability for production LLM systems (quality, cost, latency).
- Production retrieval experience with vector databases and/or search engines (OpenSearch, Elasticsearch).
- Modern Python stack proficiency, including FastAPI, Pydantic, async/await, and modern dependency management.
- Demonstrated ability to leverage AI-native workflows and tools like Claude Code/Codex to accelerate development.
- Experience with Docker, Kubernetes, and AWS.
- US citizenship required due to DoD contracting and FedRAMP compliance.
Skills
- Python
- Kotlin
- TypeScript/Node.js
- FastAPI
- Pydantic
- Anthropic LLM SDKs
- OpenAI LLM SDKs
- Claude Code
- Codex
- Agent SDKs
- MCP servers
- Agent Skills standards
- Langfuse
- Vector databases
- OpenSearch
- Embedding models
- PostgreSQL
- Amazon S3
- Kafka
- Kinesis
- Docker
- Kubernetes
- AWS EKS
- DataDog
- OpenTelemetry
- Hybrid search
- Reranking
- Query expansion
- Contextual compression
- GraphRAG
- Agentic RAG
- Knowledge graphs
- Graph databases
Location
- Remote
- Twin Cities area (Minneapolis, Saint Paul)
Work Type
- Remote
- Full-time
Experience Level
- 7+ years of professional software engineering experience
- 3+ years focused on AI/ML or data engineering
About the Company
- Collaboration.Ai is a mission-focused, AI-powered software and services company based in Minnesota.
- We unite people, technology, and purpose to accelerate breakthroughs that transform industries, empower communities, and create a more sustainable future.
- We collaborate with organizations across the defense ecosystem, helping them navigate complex challenges and drive transformative change.
- Our Products include NetworkOS, an AI-powered platform for real-time alignment and actionable insights, and CrowdVector, an integrated solution marketplace and innovation management platform.