{"schemaVersion":"jobsearcher.job.v1","id":"aa8e613abf7720a42cce4ffd","url":"https://jobsearcher.com/jobs/aa8e613abf7720a42cce4ffd","canonicalUrl":"https://jobsearcher.com/jobs/aa8e613abf7720a42cce4ffd","title":"Forward Deployed Engineer","description":"Senior AI/ML Engineer — Forward Deployed Engineer\n\nWork Location: Washington, DC — Hybrid; at least three days onsite per week\n\nPosition Summary\n\nThe Senior AI/ML Engineer serves as a senior Forward Deployed Engineer and accountable technical lead supporting one or more Federal Agencies. The role translates ambiguous mission needs into secure, usable, accessible, and Government-owned AI capabilities. The incumbent provides hands-on architecture, engineering, AI assurance, delivery leadership, and stakeholder advisory support across discovery, design, development, testing, deployment, operations, and knowledge transfer.\n\nEssential Responsibilities\n\nLead agency-specific AI solution discovery, feasibility analysis, architecture, engineering design, integration, deployment, and operational support.\n\nTranslate incomplete mission needs into measurable problem statements, user needs, acceptance criteria, prioritized backlogs, experiments, and release plans.\n\nArchitect and implement AI-enabled applications using appropriate methods, including classical ML, document intelligence, NLP, LLM integrations, RAG, agentic workflows, multi-agent patterns, MCP server-based components, APIs, and deterministic automation.\n\nSelect the simplest safe and effective solution rather than defaulting to generative AI.\n\nLead the technical design of data flows, identity and access controls, trust boundaries, fallback and rollback methods, observability, and resilience.\n\nDirect MLOps/LLMOps practices, including version control, CI/CD, model/artifact registration, evaluation, monitoring, retraining or refresh workflows, and release management.\n\nDefine and oversee AI evaluation methods for accuracy, groundedness, relevance, hallucination, calibration, fairness, robustness, explainability, and operational impact.\n\nLead AI-specific adversarial testing for prompt injection, jailbreaks, data poisoning, retrieval manipulation, insecure tool use, agent-loop failures, secrets exposure, model extraction, and vector-store corruption.\n\nEnsure human-in-the-loop controls, documented override and escalation mechanisms, audit logging, and clear distinction between AI-generated and human-verified outputs.\n\nProduce or direct production-ready architecture artifacts, technical documentation, model cards, test evidence, SBOM inputs, operational runbooks, and release packages.\n\nCoordinate directly with Federal Agency CIO/OCIO organizations, AI governance teams, enterprise platform teams, cloud organizations, architecture review boards, security authorization teams, privacy, accessibility, legal, records, and other Government stakeholders.\n\nSupport RAIA, PIA/PTA, ATO/RMF, SSP, SAP, SAR, POA&M, and related compliance documentation.\n\nEnsure custom code, prompts, configurations, models, datasets, embeddings, and deployment assets are developed and maintained in Government-controlled repositories.\n\nServe as the technical primary point of contact for assigned Federal Agency stakeholders and provide planned/unplanned coverage for other key personnel.\n\nAct as, or work directly with, the designated Security Lead to certify security evidence for each applicable deliverable.\n\nMinimum Qualifications\n\nBachelor’s degree in computer science, data science, software engineering, information systems, statistics, engineering, or comparable field; Master’s degree preferred.\n\nSeven or more years of hands-on experience in AI/ML engineering, software engineering, cloud engineering, enterprise architecture, or data engineering, including delivery of AI-enabled systems.\n\nAt least two years of hands-on production experience in one or more of generative AI, LLM integration, RAG, model evaluation, AI agents, or production AI system operations.\n\nDemonstrated ability to deliver across AI, application development, data engineering, UX, integration, and operations in ambiguous environments.\n\nDemonstrated experience designing secure enterprise or Federal AI architectures, including cloud AI platforms, APIs, data pipelines, vector databases, model orchestration, MCP, evaluation systems, and MLOps.\n\nWorking knowledge of Federal or comparably regulated security, privacy, accessibility, Responsible AI, ATO/RMF, FISMA, FedRAMP, and PII/CUI protection practices.\n\nDemonstrated experience advising Government or executive stakeholders and leading technical delivery across multiple teams.\n\nRequired Certification Evidence\n\nThe candidate must hold and provide verifiable evidence of at least one relevant AI/ML credential, such as:\n\nAzure AI / Generative AI certification\n\nAWS Generative AI or Machine Learning certification\n\nGoogle Cloud Generative AI / ML credential\n\nDatabricks Generative AI Engineer credential\n\nNVIDIA Generative AI credential\n\nCertified Artificial Intelligence Professional (CAIP)\n\nEquivalent recognized AI/ML certification\n\nCertification evidence must be supplied at onboarding and annually thereafter. Self-attestation is not sufficient.\n\nPreferred Qualifications\n\nDirect experience supporting Federal Agencies, especially systems involving PII, CUI, public-facing digital services, grants, enforcement, benefits, claims, or case-management operations.\n\nExperience with AWS, Azure AI Foundry, Google Vertex AI, AWS Bedrock, GitHub Copilot/VS Code, GitLab, JIRA, and Government-controlled CI/CD pipelines.\n\nExperience with accessibility-by-design and delivery of systems meeting Section 508/WCAG 2.1 A/AA.\n\nExperience supporting independent testing, red teaming, security assessment, and production ATO activities.","company":"Oran","rawCompany":"oran","city":"Reston","state":"VA","isRemote":false,"isActive":false,"createdAt":"2026-09-27T12:12:55.988Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Forward Deployed Engineer","description":"Senior AI/ML Engineer — Forward Deployed Engineer\n\nWork Location: Washington, DC — Hybrid; at least three days onsite per week\n\nPosition Summary\n\nThe Senior AI/ML Engineer serves as a senior Forward Deployed Engineer and accountable technical lead supporting one or more Federal Agencies. The role translates ambiguous mission needs into secure, usable, accessible, and Government-owned AI capabilities. The incumbent provides hands-on architecture, engineering, AI assurance, delivery leadership, and stakeholder advisory support across discovery, design, development, testing, deployment, operations, and knowledge transfer.\n\nEssential Responsibilities\n\nLead agency-specific AI solution discovery, feasibility analysis, architecture, engineering design, integration, deployment, and operational support.\n\nTranslate incomplete mission needs into measurable problem statements, user needs, acceptance criteria, prioritized backlogs, experiments, and release plans.\n\nArchitect and implement AI-enabled applications using appropriate methods, including classical ML, document intelligence, NLP, LLM integrations, RAG, agentic workflows, multi-agent patterns, MCP server-based components, APIs, and deterministic automation.\n\nSelect the simplest safe and effective solution rather than defaulting to generative AI.\n\nLead the technical design of data flows, identity and access controls, trust boundaries, fallback and rollback methods, observability, and resilience.\n\nDirect MLOps/LLMOps practices, including version control, CI/CD, model/artifact registration, evaluation, monitoring, retraining or refresh workflows, and release management.\n\nDefine and oversee AI evaluation methods for accuracy, groundedness, relevance, hallucination, calibration, fairness, robustness, explainability, and operational impact.\n\nLead AI-specific adversarial testing for prompt injection, jailbreaks, data poisoning, retrieval manipulation, insecure tool use, agent-loop failures, secrets exposure, model extraction, and vector-store corruption.\n\nEnsure human-in-the-loop controls, documented override and escalation mechanisms, audit logging, and clear distinction between AI-generated and human-verified outputs.\n\nProduce or direct production-ready architecture artifacts, technical documentation, model cards, test evidence, SBOM inputs, operational runbooks, and release packages.\n\nCoordinate directly with Federal Agency CIO/OCIO organizations, AI governance teams, enterprise platform teams, cloud organizations, architecture review boards, security authorization teams, privacy, accessibility, legal, records, and other Government stakeholders.\n\nSupport RAIA, PIA/PTA, ATO/RMF, SSP, SAP, SAR, POA&M, and related compliance documentation.\n\nEnsure custom code, prompts, configurations, models, datasets, embeddings, and deployment assets are developed and maintained in Government-controlled repositories.\n\nServe as the technical primary point of contact for assigned Federal Agency stakeholders and provide planned/unplanned coverage for other key personnel.\n\nAct as, or work directly with, the designated Security Lead to certify security evidence for each applicable deliverable.\n\nMinimum Qualifications\n\nBachelor’s degree in computer science, data science, software engineering, information systems, statistics, engineering, or comparable field; Master’s degree preferred.\n\nSeven or more years of hands-on experience in AI/ML engineering, software engineering, cloud engineering, enterprise architecture, or data engineering, including delivery of AI-enabled systems.\n\nAt least two years of hands-on production experience in one or more of generative AI, LLM integration, RAG, model evaluation, AI agents, or production AI system operations.\n\nDemonstrated ability to deliver across AI, application development, data engineering, UX, integration, and operations in ambiguous environments.\n\nDemonstrated experience designing secure enterprise or Federal AI architectures, including cloud AI platforms, APIs, data pipelines, vector databases, model orchestration, MCP, evaluation systems, and MLOps.\n\nWorking knowledge of Federal or comparably regulated security, privacy, accessibility, Responsible AI, ATO/RMF, FISMA, FedRAMP, and PII/CUI protection practices.\n\nDemonstrated experience advising Government or executive stakeholders and leading technical delivery across multiple teams.\n\nRequired Certification Evidence\n\nThe candidate must hold and provide verifiable evidence of at least one relevant AI/ML credential, such as:\n\nAzure AI / Generative AI certification\n\nAWS Generative AI or Machine Learning certification\n\nGoogle Cloud Generative AI / ML credential\n\nDatabricks Generative AI Engineer credential\n\nNVIDIA Generative AI credential\n\nCertified Artificial Intelligence Professional (CAIP)\n\nEquivalent recognized AI/ML certification\n\nCertification evidence must be supplied at onboarding and annually thereafter. Self-attestation is not sufficient.\n\nPreferred Qualifications\n\nDirect experience supporting Federal Agencies, especially systems involving PII, CUI, public-facing digital services, grants, enforcement, benefits, claims, or case-management operations.\n\nExperience with AWS, Azure AI Foundry, Google Vertex AI, AWS Bedrock, GitHub Copilot/VS Code, GitLab, JIRA, and Government-controlled CI/CD pipelines.\n\nExperience with accessibility-by-design and delivery of systems meeting Section 508/WCAG 2.1 A/AA.\n\nExperience supporting independent testing, red teaming, security assessment, and production ATO activities.","datePosted":"2026-09-27T12:12:55.988Z","dateModified":"2026-09-27T12:12:55.988Z","hiringOrganization":{"@type":"Organization","name":"Oran","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Reston","addressRegion":"VA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"aa8e613abf7720a42cce4ffd"},"url":"https://jobsearcher.com/jobs/aa8e613abf7720a42cce4ffd"}}