{"id":169244,"url":"https://alion.io/job/minfy-technologies-ai-engineer","title":"AI Engineer","company":{"id":59584,"name":"Minfy Technologies","domain":"minfytech.com","url":"https://alion.io/company/minfy-technologies","size_band":"201-500","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"B","score":84,"open_postings":20,"ghost_share":0,"stale_share":0.45,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["McLean, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":70,"max":90,"currency":"USD","period":"hour","gross":null,"usd_annual":180000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true}],"status":"live","first_seen_at":"2026-08-18T08:56:11Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-10-18T04:51:47Z","board_verified":true,"closed_at":null,"days_open":60,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":60},"description":"Senior AI / LLM Engineer\nLocation: McLean, Virginia\nEmployment Type: Full-Time\nFunction: AI / Machine Learning Engineering\nEligibility: Must be a U.S. Person (required for access to an ITAR / export-controlled environment)\nAbout the Company\nMinfy is a technology consulting company that helps enterprises solve business problems with AI. Minfy’s advanced engineering capability enables enterprises to build intelligent applications, modernize legacy systems and operate mission-critical applications. Minfy combines cloud, data, and AI engineering capabilities with systems knowledge, governance frameworks and industry expertise to deliver solutions that enable faster time to market, lower cost, and better competitive differentiation for customers. Minfy was a Leader and Star Performer in the Everest Group’s AWS Services PEAK Matrix® Assessment 2025 and a Seasoned Vendor in AIM Research’s PeMa Quadrant for GenAI Service Providers 2026. Minfy is a premier tier consulting partner of AWS and has certifications and competencies across Anthropic, AWS, Microsoft, and Google Cloud. Founded in 2016, Minfy serves Fortune 500 companies, large enterprises, high-growth businesses, and startups across the world.\nAbout the Role\nWe are hiring a Senior AI / LLM Engineer to design and build LLM-powered features and applications across a range of use cases. You will work hands-on across the modern AI engineering stack - retrieval, integration, evaluation, and production hardening - and take ownership of significant pieces of the system from design through deployment. Retrieval-augmented generation over large, real-world enterprise data is a prominent part of the work, alongside platform integration and LLM-driven analysis. You will set technical direction within your area, make sound trade-offs under ambiguity, and help raise the bar for engineers around you.\nWhat You'll Do\nOwn the design and delivery of LLM-powered features end-to-end - from problem framing and architecture through production deployment and iteration.\nBuild and tune retrieval-augmented generation (RAG) pipelines over large, heterogeneous enterprise data - ingestion, chunking, embeddings, indexing, and entity/relationship modeling - with a focus on retrieval accuracy and closing coverage gaps.\nDesign and build data ingestion and indexing pipelines that reliably capture content, map identities across systems, and support incremental/resumable sync at scale.\nIntegrate LLMs (via managed platforms such as Amazon Bedrock) for question answering, analysis, and other tasks, preserving sessions, sources, and citations.\nIntegrate with enterprise platforms and collaboration tools through their APIs, including SSO/OAuth flows and event-driven bot/app patterns.\nDesign permission-bounded access and correct attribution in multi-user contexts, so the system never surfaces data a user could not already access.\nEstablish evaluation practices for retrieval quality and answer correctness, and use them to drive iteration and catch regressions.\nAdd observability, logging, and audit trails, and lead debugging of quality and performance issues in production.\nGuide and mentor other engineers through design and code reviews, and contribute to shared standards.\nRequired Qualifications\n6-8 years of software engineering experience, with at least 2 years building with LLMs or applied ML in production.\nStrong proficiency in Python (or comparable) and strong engineering fundamentals - testing, version control, clean and maintainable code.\nDeep hands-on experience with RAG systems: embeddings, vector databases, chunking/indexing, and a strong track record diagnosing and improving retrieval quality.\nExperience designing and building data ingestion/ETL pipelines over large, messy, real-world datasets.\nStrong experience integrating third-party APIs into backend services, including authentication flows (OAuth/SSO) and webhook/event-driven patterns.\nHands-on experience with LLM APIs (e.g., Anthropic, OpenAI, or similar) and orchestration frameworks.\nExperience building and relying on evaluations for model and retrieval outputs.\nExperience with knowledge graphs or entity-relationship modeling for retrieval.\nExperience building multi-user or multi-tenant systems with scoped permissions and audit requirements.\nFamiliarity with observability and tracing for LLM or data pipelines.\nA rigorous approach to data access, permissions, and handling sensitive information.\nExperience taking systems to production on a major cloud platform (AWS preferred), and a track record of owning features independently.\nPreferred Qualifications\nExperience with Amazon Bedrock or other managed LLM platforms.\nExperience integrating with enterprise collaboration platforms (chat, wikis, ticketing) via their APIs.\nMLOps exposure: Docker, CI/CD, production service deployment.\nBachelor's or advanced degree in Computer Science, Engineering, or a related field - or equivalent practical experience.\nCompensation\nThe base salary range for this position is $150,000 - $200,000 USD annually, depending on experience, qualifications, and internal equity. This range reflects the anticipated base pay for this role and does not include potential bonuses, benefits, or other forms of compensation.\nEqual Employment Opportunity\nMinfy is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants, and we do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, veteran status, marital status, or any other characteristic protected by applicable federal, state, or local law. All qualified applicants will receive consideration for employment without regard to these characteristics.\nRecruiting Fraud Notice: Minfy Technologies never requests payment, banking details, or personal financial information as part of our recruitment or hiring process. All official communication regarding job opportunities at Minfy will come from an email address ending in @minfytech.com. If you receive a suspicious job offer claiming to be from Minfy, or are asked to make any payment or share sensitive personal/financial information, please do not respond and report it to us immediately at . 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