{"id":3841774,"url":"https://alion.io/job/chiri-applied-ai-engineer-document-intelligence","title":"Applied AI Engineer – Document Intelligence","company":{"id":4423461,"name":"Chiri","domain":"chiri.ai","url":"https://alion.io/company/chiri-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Gem","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"ChatGPT","optional":false},{"name":"Claude","optional":false},{"name":"CMMC","optional":false},{"name":"Gemini","optional":false},{"name":"Hugging Face","optional":false},{"name":"Linux","optional":false},{"name":"llama.cpp","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OCR","optional":false},{"name":"Ollama","optional":false},{"name":"OpenAI","optional":false},{"name":"Outlook","optional":false},{"name":"Perplexity","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"SBOM","optional":false},{"name":"SQL","optional":false},{"name":"SQLite","optional":false},{"name":"TGI","optional":false},{"name":"vLLM","optional":false},{"name":"VLM","optional":false},{"name":"Windows Server","optional":false}],"status":"live","first_seen_at":"2026-11-05T21:35:24Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T12:41:32Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About Chiri\nChiri collapses fragmented software into one governed operating layer for mid-sized businesses: companies with valuable workflows and real urgency, but no internal AI organization to take a use case to production.\nWe combine our platform with forward-deployed expertise to connect customers' existing systems, build what they need, and operate it in production.\nIncreasingly, buyers are asking ChatGPT, Perplexity, Gemini, Claude, and other AI systems what they should use. Chiri must be one of their top answers.\nRole Overview\nWe're looking for an Applied AI Engineer to design, build, and deploy an AI-powered document intelligence solution for a Silicon Valley electronics manufacturer operating in the defense supply chain.\nThis is a hands-on engineering role focused on building a production-ready, zero-egress RFQ (Request for Quote) screening service that processes complex technical documents entirely within the client's security boundary.\nThe solution will help the client's estimating team review incoming RFQ packages, identify potential disqualifiers, extract quality requirements, analyze material specifications, and flag missing or conflicting information.\nThe service will process approximately 70 RFQ packages per day, each containing 3-30 pages of engineering drawings and 10-20 quality clauses. Today, reviewing a single package can take an estimator 1.5-3 hours.\nYou'll build an end-to-end document processing pipeline using Python, open-weight multimodal models, and modern AI engineering techniques. The system must produce structured, citation-grounded outputs, with every extracted value traceable to its source document and page.\nThis role requires strong technical ownership, practical experience deploying AI models locally, and a disciplined approach to security and data handling. You'll work directly with Chiri's engineering team and the client's estimating, quality, and IT stakeholders to deliver a solution that supports human decision-making.\nThe system supports human decisions. It never makes a bid or decline decision itself.\nKey Responsibilities\nDesign, develop, and maintain the end-to-end RFQ screening pipeline, from PDF ingestion and document classification to AI-powered extraction, rule application, and structured report generation.\nBuild document-processing workflows that extract text from PDFs, render drawing pages as images, classify document types, and process complex technical content.\nDeploy and optimize open-weight multimodal models for fully offline inference on a single GPU server.\nSelect appropriate models and quantization strategies to operate within approximately 48 GB of GPU memory.\nDevelop and refine prompts and extraction logic for quality clauses, material tables, engineering drawings, and technical specifications.\nImplement citation-grounded extraction, ensuring every value includes a page reference and supporting source text.\nBuild validation and rule-based logic to identify potential disqualifiers, compare extracted materials against stock lists, and flag missing or conflicting values.\nDevelop the same Chiri application layer against Azure OpenAI through Azure Government and a private endpoint, enabling a clean comparison between local inference and the Azure-based option.\nImplement a local data store and audit logging system using SQLite or SQL Server Express to record model calls, input file hashes, and generated reports.\nPackage application releases as signed, offline-installable artifacts with pinned dependencies, a Software Bill of Materials (SBOM), release hashes, and supporting documentation.\nProduce data-flow diagrams and technical documentation for each deployment option to support the client's System Security Plan.\nBuild an evaluation harness that allows the client's estimating team to score report accuracy by field.\nImprove extraction quality using aggregate evaluation metrics and scorecards without accessing or receiving the client's underlying production data.\nLead or support the onsite Week 1 working session with estimating, quality, and IT stakeholders to define accuracy targets, select sample RFQs, and establish the disqualifier list.\nSupport the client's IT team with installation, configuration, and troubleshooting within the agreed security and access boundaries.\nCollaborate with Chiri engineers and client stakeholders to translate business requirements into practical, secure, and maintainable technical solutions.\nDeliver a final accuracy scorecard comparing the local and Azure-based options, broken down by field.\nContribute technical recommendations and implementation estimates for the potential Phase 1 engagement.\nRequired Skills and Experience\n5+ years of professional software engineering experience, with a strong track record of shipping production systems.\nStrong professional experience with Python, including backend development, data processing, and AI-powered applications.\nHands-on experience building LLM or vision-language model (VLM) extraction pipelines for complex documents, including PDFs, scanned pages, tables, and technical drawings.\nExperience implementing structured AI outputs, source grounding, and citation-based extraction.\nHands-on experience self-hosting open-weight models on GPU hardware, using tools such as vLLM, llama.cpp, Ollama, or Hugging Face TGI.\nExperience selecting and optimizing models and quantization levels for local inference, including working within GPU memory constraints.\nExperience building document-processing pipelines that include PDF text extraction, page rendering, OCR, and document classification.\nExperience deploying AI systems in air-gapped or offline environments, including offline installation, pinned dependencies, and no runtime internet access.\nExperience with Azure OpenAI and Azure networking, including private endpoints and keyless or customer-managed authentication.\nStrong understanding of software supply-chain security, including signed releases, SBOM generation, and reproducible builds with published hashes.\nExperience building evaluation frameworks for AI extraction systems, including per-field accuracy metrics and systematic quality improvement.\nSolid SQL skills and experience designing local data stores and audit logs using SQLite, SQL Server, or comparable databases.\nStrong understanding of software architecture, system design, and engineering best practices.\nExperience owning technical deliverables from design through implementation, deployment, and ongoing support.\nComfortable working onsite with non-technical stakeholders, including estimating, quality, and IT teams.\nAbility to explain AI behavior, limitations, accuracy, and technical tradeoffs in clear, practical language.\nStrong analytical and problem-solving skills.\nExcellent written and verbal communication skills in English.\nA disciplined approach to data security and the ability to work effectively without direct access to production data.\nPreffered Qualifications\nWorking knowledge of NIST SP 800-171, CMMC, DFARS 252.204-7012, CUI handling, and System Security Plans.\nExperience delivering AI solutions in defense, aerospace, electronics manufacturing, or other regulated environments.\nFamiliarity with Azure Government, including service and model availability, quotas, private networking, and abuse-monitoring exemption processes.\nExperience working with printed circuit board (PCB) manufacturing specifications, including IPC standards, laminate materials and Tg, copper weight, surface finishes, solder mask, and rigid-flex materials.\nFamiliarity with electronics quoting, manufacturability analysis, CAM workflows, or tools such as Frontline Integr8tor.\nExperience interpreting engineering drawings, fabrication notes, quality clauses, or flow-down requirements in aerospace and defense purchase orders.\nExperience deploying applications on Windows Server or Linux virtual machines managed by a client's IT team.\nExperience writing installation guides, operational runbooks, and technical documentation for customer-managed environments.\nFamiliarity with Microsoft Exchange, Outlook, and Access integrations.\nExperience building rules engines or spreadsheet-driven business rules that can be maintained by business users.\nExperience with agentic and AI-assisted development workflows.\nExperience in consulting, forward-deployed engineering, or client-facing technical delivery.\nDemonstrated ability to deliver measurable business outcomes through AI and automation.\nWhat We Offer\nThe opportunity to build a real-world AI solution for a complex, high-impact manufacturing use case.\nMeaningful technical ownership across AI model deployment, document intelligence, system architecture, and secure software delivery.\nThe opportunity to work with cutting-edge open-weight multimodal models and local inference infrastructure.\nDirect collaboration with experienced engineers, company leadership, and client stakeholders.\nA focused, fast-paced engagement where engineering decisions have a tangible impact on business operations.\nThe opportunity to help define how enterprise-ready AI can operate within strict security and compliance boundaries.\nPotential to extend the engagement into Phase 1, subject to project needs and mutual agreement.\nSecurity & Data Boundaries\nThe data boundary defines the project.\nThe service runs entirely inside the client's CUI boundary and makes no outbound connections.\nNo telemetry.\nNo automatic updates.\nNo external license checks.\nNo transfer of client RFQs, drawings, or extracted data to Chiri.\nNo copying or storing client production data outside the approved environment.\nChiri delivers a signed installation package with an SBOM and releases hashes. The client's IT team installs and operates the solution.\nSupport takes place through client-controlled screen-sharing. The engineer does not log in to client systems, receive client data, or take control of the client's keyboard.\nThe engineer must be comfortable developing, evaluating, and improving the solution while respecting these strict boundaries.\nEngagement Details\n Initial duration: Approximately 6 weeks (Phase 0).\nPotential extension: Phase 1, subject to project requirements and agreement.\nU.S. person status. Must be eligible to work under applicable U.S. person requirements, sign a written attestation, and comply with DFARS 252.204-7012 and CUI handling requirements.\nOur SCOUT Values\nOur values are not wall art. They describe how we expect people to operate.\nSeek to Understand\nAssume you might be wrong. Clarity before action.\nCall It Out\nSee something, say something. Say the hard thing.\nOwnership Mentality\n\"That's not my job\" does not exist.\nUse Human Judgment\nJudgment, context, and care come from us. AI is a tool.\nTalk with Precision\nConcise. 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