{"id":59822,"url":"https://alion.io/job/true-zero-technologies-aiml-engineer-r-00194","title":"AI/ML Engineer (R-00194)","company":{"id":8178,"name":"True Zero Technologies","domain":"truezerotech.com","url":"https://alion.io/company/true-zero-technologies","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":95,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":155,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":132000,"max_usd":290000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"FedRAMP","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Zero Trust","optional":false}],"status":"live","first_seen_at":"2026-08-14T18:36:43Z","employer_posted_date":"2026-08-14","last_verified_at":"2026-10-19T02:52:27Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":65},"description":"True Zero Technologies, a veteran-owned small business, was founded on the principle that the purposeful enablement of people and technology in an organization directly ties to the quality of its outcomes. True Zero recognizes that those outcomes begin and end with our people, and that is what we have built a community of like-minded, driven, and passionate individuals and innovators who are aligned in a common goal of delivering top-tier services to our customers. Our culture and commitment have been recognized through numerous accolades, including being named one of the Best Places to Work in 2023 in two categories (“Prosperous and Thriving” ($5MM-$50MM in gross revenue) and “Mid-Atlantic Region” (DC, DE, MD, NC, VA, WV)), and again in 2025 as a Best Places to Work honoree. In addition, True Zero earned coveted spots on the Inc. 5000 list of fastest-growing companies in America in 2022, 2023, and 2025, a testament to our sustained growth driven by our people-first approach and unwavering dedication to excellence.\nThe AI/ML Engineer is responsible for designing, building, integrating, deploying, and operating artificial intelligence and machine learning capabilities that support mission and business workflows in secure government cloud environments. This role spans the AI/ML lifecycle, including data preparation, model and application development, evaluation, deployment, monitoring, and ongoing optimization.\nThe engineer will leverage FedRAMP-authorized services available within AWS GovCloud, while ensuring all AI/ML solutions align with applicable security, data handling, compliance, and Zero Trust requirements. The ideal candidate has strong Python development skills, experience with modern AI/ML architectures and data pipelines, and practical knowledge of deploying and evaluating models in cloud environments.\nJob Responsibilities\nAI/ML Engineering & Mission Integration\nBuild and integrate AI/ML capabilities supporting mission workflows using FedRAMP-authorized services available in AWS GovCloud, from data preparation through deployment, evaluation, and monitoring.\nDesign, develop, test, and operationalize AI/ML solutions aligned to defined mission and business requirements.\nTranslate operational use cases into scalable AI/ML architectures, services, and integration patterns.\nDevelop APIs, services, and application integrations that expose AI/ML capabilities to mission applications and enterprise platforms.\nCollaborate with application engineers, data engineers, cloud engineers, cybersecurity teams, and mission stakeholders throughout the solution lifecycle.\nConduct technical evaluations and proof-of-concept implementations to determine whether proposed AI/ML technologies are appropriate for the mission, security environment, and available GovCloud services.\nMaintain technical documentation covering architecture, model behavior, interfaces, dependencies, deployment procedures, and operational requirements.\nPython & AI/ML Development\nDevelop production-quality AI/ML applications and supporting services using Python.\nBuild reusable Python modules, services, utilities, and automation supporting data processing, inference, evaluation, and system integration.\nApply standard software engineering practices including source control, automated testing, code review, dependency management, and CI/CD.\nDevelop and integrate machine learning models, foundation models, or AI services based on approved use cases and architecture.\nOptimize AI/ML application performance, reliability, scalability, and resource utilization.\nTroubleshoot issues involving model behavior, data quality, application integration, cloud services, and runtime environments.\nGenerative AI & Retrieval-Augmented Generation\nDesign and implement Generative AI and Retrieval-Augmented Generation (RAG) patterns when included within the approved solution scope.\nDevelop workflows for document ingestion, parsing, chunking, embedding generation, indexing, retrieval, prompt construction, and model inference.\nIntegrate approved large language models and foundation-model services with enterprise applications and mission data sources.\nEvaluate retrieval quality, response relevance, groundedness, hallucination risk, and overall solution effectiveness.\nDevelop prompt-management, model-routing, and orchestration patterns where appropriate.\nImplement safeguards and validation mechanisms to reduce the risk of inappropriate, inaccurate, or unauthorized model outputs.\nSupport secure integration of vector stores, search services, knowledge repositories, and other components required by RAG architectures.\n\nJob Qualifications\nBachelor’s degree in Cybersecurity, Computer Science, Information Technology, Information Systems, or a related technical discipline.\nExperience with Generative AI, foundation models, and RAG architectures, where applicable to the program scope.\nExperience with embeddings, vector search, semantic retrieval, prompt engineering, and LLM evaluation.\nExperience with supervised or unsupervised machine learning, feature engineering, model training, and model selection where traditional ML is in scope.\nFamiliarity with MLOps or LLMOps practices for model and application lifecycle management.\nExperience developing automated model and application evaluation frameworks.\nFamiliarity with responsible AI concepts, including model limitations, bias evaluation, explainability, traceability, and human oversight.\nExperience deploying containerized workloads and microservices.\nExperience working in government, defense, regulated, or other security-sensitive 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