Blencorp2d ago

New

USD 130000–150000/yr

AI Engineer

United States·WashingtonRemoteFull-Time, W-2mid

Machine Learning EngineerData

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Quick Summary

Overview

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…

Technical Tools

anthropicawsazuredockerfastapiflaskgcpkubernetesopenaipythonsqltypescriptci-cdcode-review

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 the Role

~5 min read

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.

Design and build agentic systems — multi-step agents that plan, call tools, retrieve context, and take action with appropriate human-in-the-loop checkpoints

Build MCP servers and clients to securely expose client data, internal tools, and APIs to LLMs in a standardized, auditable way

Ship LLM-powered applications: copilots, document intelligence, search, summarization, and workflow automation

Design and maintain RAG pipelines — chunking, embeddings, vector stores, retrieval, reranking, and grounding

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

Develop evals and observability for agents and AI features so we know what's working in production and what's regressing

Apply prompt engineering, structured outputs, function/tool calling, and guardrails to make agent behavior predictable

Write production Python backends and APIs that expose AI capabilities to web and mobile clients

Collaborate with engineers, designers, and product folks to scope what AI should (and shouldn't) do in a given product

Help shape responsible AI practices for federal use — privacy, security, auditability, and human oversight

5+ years of professional software engineering experience, with at least 1 year shipping LLM-based or AI-powered features to production

Hands-on experience designing or building agentic systems — tool calling, multi-step reasoning, planning loops, or agent orchestration (LangGraph, CrewAI, OpenAI Agents SDK, Claude tool use, or equivalent)

Working knowledge of the Model Context Protocol (MCP) — or demonstrated ability to pick it up quickly, plus familiarity with the broader landscape of agent/tool standards

Strong Python and experience building and deploying backend services and APIs (FastAPI, Flask, or similar)

Hands-on experience with at least one major LLM provider (OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or open-weight models via vLLM/Ollama)

Working knowledge of RAG: embeddings, vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar), and retrieval evaluation

Comfort with prompt engineering, structured outputs (JSON mode, schemas), and tool/function calling

Experience writing evals — even lightweight ones — for non-deterministic systems

Solid SQL and experience with relational and unstructured data

Familiarity with at least one cloud platform (AWS, Azure, or GCP)

Git, code review, and modern collaborative workflows

Strong written and verbal communication — you can explain AI tradeoffs to non-technical stakeholders

Experience authoring MCP servers for non-trivial systems (databases, internal APIs, document stores)

Experience with eval and observability platforms (Braintrust, LangSmith, Langfuse, Arize, or custom harnesses)

Multi-agent orchestration patterns and experience reasoning about agent failure modes

Fine-tuning, distillation, or LoRA experience where it actually moved the needle

Docker, Kubernetes, and CI/CD for AI workloads

TypeScript/Node for full-stack AI features

Streaming UIs (SSE, WebSockets) and token-level UX patterns

Experience with caching, prompt compression, and cost/latency optimization at scale

Background supporting federal or government clients

Awareness of NIST AI RMF, FedRAMP, or related responsible-AI frameworks

Must be a US Citizen or legal resident, able to work domestically

Must be able to attain a low-level security clearance

Must work from the United States

Work from anywhere in the US

Competitive pay

Contribution toward health benefits

High-visibility federal projects with real impact

Small team where your ideas actually ship

Generous exposure to the latest AI tooling and models

We're a small, creative, highly technical team. Our heroes are the scrappy folks who dare to dream and do great things. We care more about doing the right thing than taking shortcuts. We finish projects. We surprise our clients with how much we genuinely care about their success. We're selective about our partners — and our people. We don't say "human resource" because you're not a resource. You're a teammate, and we'll treat you like one.

We will treat you fairly

We give you space to grow personally and professionally

We will hear your ideas even when we disagree

We will be honest about our challenges and equitable with our success

We will tell you the truth, even when it's difficult