{"id":3556257,"url":"https://alion.io/job/redhorse-senior-aiml-engineer","title":"Senior AI/ML Engineer","company":{"id":684975,"name":"Redhorse","domain":"redhorsecorp.com","url":"https://alion.io/company/redhorsecorp","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":77,"open_postings":16,"ghost_share":0,"stale_share":0.938,"repost_share":0,"time_to_fill_p50_days":41,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":180000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Confluence","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Jira","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Office","optional":false},{"name":"NLP","optional":false},{"name":"pySpark","optional":false},{"name":"QlikSense","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"Python","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-30T20:55:29Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-11-03T04:47:05Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About the Role\nRedhorse transforms the way government uses data and technology. To support this mission, we are seeking an Artificial Intelligence (AI)/Machine Learning (ML) Engineer to support Condition-Based Maintenance Plus (CBM+) initiatives that improve aircraft sustainment and optimize maintenance and logistics for the United States Air Force (USAF).\nIn this role, you will serve as a key architect in the CBM+ ecosystem, bridging the gap between cutting-edge development and operational deployment. Working within cross-functional teams in an Agile environment, you will translate complex predictive models into production-grade systems that directly enhance fleet readiness and sustainment efficiency. This is a unique opportunity to operate at the intersection of aerospace logistics and advanced MLOps, delivering high-impact solutions for our nation's defense.\nKey Responsibilities\nLead CBM+ AI/ML initiatives from concept through production deployment, ensuring technical solutions align with mission outcomes, business objectives, and enterprise architecture standards.\nArchitect and oversee scalable data pipelines, including data ingestion, ETL/ELT processing, feature engineering, data validation, and sustainment of production data workflows.\nDesign, develop, and optimize machine learning, deep learning, and generative AI models to solve complex operational problems and improve predictive maintenance capabilities.\nPerform advanced exploratory data analysis, statistical modeling, and data visualization to uncover trends, correlations, risk indicators, and actionable insights that guide product and mission decisions.\nBuild, maintain, and extend knowledge graph capabilities to support semantic reasoning, data integration, and AI-enabled decision support.\nProvide technical leadership to multi-functional engineering teams, mentoring junior engineers and guiding best practices for model development, MLOps, testing, documentation, and secure deployment.\nCollaborate with software, data, cloud, and product teams to integrate trained models into production applications, APIs, and operational workflows.\nEstablish monitoring strategies for data quality, model performance, drift, bias, and degradation, and lead mitigation efforts to maintain reliable AI/ML system performance.\nRequired Experience/Clearance\nAn active Secret security clearance is mandatory for this position. Applicants must currently possess an active Secret clearance to be considered. Candidates without an active clearance will not be considered.\nEducation & Experience: Master’s degree in a STEM (Science, Technology, Engineering, or Mathematics) field or proven equivalent professional experience.\nNatural Language Processing (NLP): Minimum of 5 years of experience incorporating applied NLP, data labeling, and entity or keyword extraction.\nAnalytics & Data Engineering: Proven proficiency with Databricks, PySpark, SQL, data governance frameworks, and technical documentation standards.\nStatistical Foundations: Deep understanding and practical application of statistical distributions for data assessment, data analysis, and predictive modeling.\nVisualization & Data Products: Experience developing dashboard or data end-products utilizing tools such as Streamlit, Qlik Sense, or similar platforms.\nTools & Methodologies: Experience using project management and productivity platforms including Jira, Confluence, Lucidchart, and Microsoft Office Suite (Word, Excel, PowerPoint).\nCommunication & Initiative: Demonstrated self-starter with strong interpersonal skills, with the proven ability to translate complex technical findings for executive and non-technical stakeholders.\nTravel: Ability to travel minimally as needed to engage with government customers and project stakeholders.\nDesired Experience\nWe encourage all candidates who meet the basic requirements to apply, even if you do not have any of the following experience:\nPh.D. in a STEM field, with a preference for Data Science, Data Analytics, or Computer Science.\nAdvanced expertise applying artificial intelligence and machine learning (AI/ML) to predictive maintenance, condition-based maintenance, or defense logistics challenges.\nDirect experience developing sensor-based failure prediction models and equipment health indicators.\nPractical experience conducting Monte Carlo simulations and calculating statistical confidence intervals.\nDemonstrated expertise in end-to-end data pipeline 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