AI Engineer
US$130,000 – US$150,000 / yr base salary
Posted 6 May 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
| Base salary | US$130,000 – US$150,000 / yr |
|---|
About BLEN
BLENers are passionate about using technology to solve real-world problems. For over 20 years, we've helped government agencies and businesses transform their digital experience — modernizing legacy systems, building cloud-native applications, and experimenting with what's just around the corner. We value long, enduring partnerships and put humans at the center of every experience. Our team thrives on turning tricky problems into solutions that are practical, accessible, and performant.
About this position
We're hiring an AI Engineer to help our federal and commercial clients ship production-grade applications powered by large language models — with a strong focus on agentic systems and MCP-based integrations.
You'll spend your time building real things: agents that take actions on behalf of users, RAG pipelines that ground answers in trusted sources, and MCP servers that securely connect models to the data and tools our clients already rely on. You'll wire up model APIs, design tool interfaces, build evals, and make sure what we ship is fast, reliable, observable, and safe.
This isn't a research role. You won't be training foundation models. You will be designing and shipping agentic AI systems that real users — including senior government stakeholders — depend on, and you'll have a strong voice in how we adopt generative AI responsibly across our portfolio.
If you get excited about agent design, tool use, MCP, evals, and the weekly firehose of new models and frameworks — and you want that energy pointed at meaningful public-sector work — this is for you.
What You'll Do
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Design and build agentic systems — multi-step agents that plan, call tools, retrieve context, and take action with appropriate human-in-the-loop checkpoints
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Build MCP servers and clients to securely expose client data, internal tools, and APIs to LLMs in a standardized, auditable way
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Ship LLM-powered applications: copilots, document intelligence, search, summarization, and workflow automation
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Design and maintain RAG pipelines — chunking, embeddings, vector stores, retrieval, reranking, and grounding
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Integrate model APIs (OpenAI, Anthropic, Bedrock, Azure OpenAI, open-weight models) and pick the right model for the job based on quality, latency, and cost
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Develop evals and observability for agents and AI features so we know what's working in production and what's regressing