{"id":763902,"url":"https://alion.io/job/blencorp-ai-engineer","title":"AI Engineer","company":{"id":670436,"name":"BLEN, Inc.","domain":"blencorp.com","url":"https://alion.io/company/blencorp","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":130000,"max":150000,"currency":"USD","period":"year","gross":null,"usd_annual":150000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Ollama","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"Arize Phoenix","optional":true},{"name":"Braintrust","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"FedRAMP","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"Langfuse","optional":true},{"name":"LangSmith","optional":true},{"name":"LoRA","optional":true},{"name":"Model Distillation","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"NIST AI RMF","optional":true},{"name":"PEFT","optional":true},{"name":"PostgreSQL","optional":true},{"name":"TypeScript","optional":true},{"name":"WebSockets","optional":true}],"status":"live","first_seen_at":"2026-05-06T20:13:17Z","employer_posted_date":"2026-05-06","last_verified_at":"2026-10-28T16:24:16Z","board_verified":true,"closed_at":null,"days_open":174,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":174},"description":"About BLEN\nBLENers 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.\nAbout this position\nWe'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.\nYou'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.\nThis 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.\nIf 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.\nWhat You'll Do\nDesign and build agentic systems - multi-step agents that plan, call tools, retrieve context, and take action with appropriate human-in-the-loop checkpoints\n\nBuild MCP servers and clients to securely expose client data, internal tools, and APIs to LLMs in a standardized, auditable way\n\nShip LLM-powered applications: copilots, document intelligence, search, summarization, and workflow automation\n\nDesign and maintain RAG pipelines - chunking, embeddings, vector stores, retrieval, reranking, and grounding\n\nIntegrate model APIs (OpenAI, Anthropic, Bedrock, Azure OpenAI, open-weight models) and pick the right model for the job based on quality, latency, and cost\n\nDevelop evals and observability for agents and AI features so we know what's working in production and what's regressing\n\nApply prompt engineering, structured outputs, function/tool calling, and guardrails to make agent behavior predictable\n\nWrite production Python backends and APIs that expose AI capabilities to web and mobile clients\n\nCollaborate with engineers, designers, and product folks to scope what AI should (and shouldn't) do in a given product\n\nHelp shape responsible AI practices for federal use - privacy, security, auditability, and human oversight\n\nBasic qualifications\n5+ years of professional software engineering experience, with at least 1 year shipping LLM-based or AI-powered features to production\n\nHands-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)\n\nWorking 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\n\nStrong Python and experience building and deploying backend services and APIs (FastAPI, Flask, or similar)\n\nHands-on experience with at least one major LLM provider (OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or open-weight models via vLLM/Ollama)\n\nWorking knowledge of RAG: embeddings, vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar), and retrieval evaluation\n\nComfort with prompt engineering, structured outputs (JSON mode, schemas), and tool/function calling\n\nExperience writing evals - even lightweight ones - for non-deterministic systems\n\nSolid SQL and experience with relational and unstructured data\n\nFamiliarity with at least one cloud platform (AWS, Azure, or GCP)\n\nGit, code review, and modern collaborative workflows\n\nStrong written and verbal communication - you can explain AI tradeoffs to non-technical stakeholders\n\nNice to Have\nExperience authoring MCP servers for non-trivial systems (databases, internal APIs, document stores)\n\nExperience with eval and observability platforms (Braintrust, LangSmith, Langfuse, Arize, or custom harnesses)\n\nMulti-agent orchestration patterns and experience reasoning about agent failure modes\n\nFine-tuning, distillation, or LoRA experience where it actually moved the needle\n\nDocker, Kubernetes, and CI/CD for AI workloads\n\nTypeScript/Node for full-stack AI features\n\nStreaming UIs (SSE, WebSockets) and token-level UX patterns\n\nExperience with caching, prompt compression, and cost/latency optimization at scale\n\nBackground supporting federal or government clients\n\nAwareness of NIST AI RMF, FedRAMP, or related responsible-AI frameworks\n\nRequirements\nMust be a US Citizen or legal resident, able to work domestically\n\nMust be able to attain a low-level security clearance\n\nMust work from the United States\n\nPerks\nWork from anywhere.\nThe chance to work on high-visibility projects and make a significant impact.\nCompetitive salary range ($130,000 - $200,000+) based on role requirements and your experience and impact.\nComprehensive medical, dental, vision, life, and disability insurance.\n\nUp to 20+ days of PTO annually.\n\n401(k) retirement savings plan.\n\nFSA/HSA and commuter benefit options.\n\nProfessional development opportunities, certifications, and continuous learning support.\n\nCompany-provided technology and access to productivity and AI tools.\n\nAdditional bonus based on performance.\nGet to know us\nWe'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.\nWhat you should expect from us\nWe will treat you fairly\n\nWe give you space to grow personally and professionally\n\nWe will hear your ideas even when we disagree\n\nWe will be honest about our challenges and equitable with our success\n\nWe will tell you the truth, even when it's difficult\n\nWhat to Expect in Our Hiring Process\nOur hiring process typically consists of 3 interview stages, after an initial screening. For engineering roles, the process may include additional technical assessments depending on the position's requirements. 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Docs: https://alion.io/developers"}}