{"id":3487020,"url":"https://alion.io/job/adt-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":1765638,"name":"ADT","domain":"adt.com","url":"https://alion.io/company/adt-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":91,"open_postings":40,"ghost_share":0,"stale_share":0.15,"repost_share":0,"time_to_fill_p50_days":88,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boca Raton, United States","Irving, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":99000,"max_usd":191000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AgentOps","optional":false},{"name":"Agile","optional":false},{"name":"CI/CD","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"Google Cloud Run","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"TensorFlow","optional":true},{"name":"XGBoost","optional":true}],"status":"live","first_seen_at":"2026-10-29T17:05:27Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-02T14:21:43Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Summary:\nAs an ML Engineer II on the AI, Automation & Data team, you will independently design, deploy, operate, and continuously improve production machine learning, generative AI, and agent-enabled solutions that deliver measurable business value. This hands-on role combines end-to-end ML product ownership with ownership of defined components within shared MLOps, Feature Store, model-observability, AgentOps, and GenAIOps capabilities.\nYou will lead moderately complex implementations, mentor junior team members and interns, and work with ML Engineers, Data Engineers, Data Scientists, Architects, Product and Domain Owners, and business partners. Enterprise architecture, organization-wide technical strategy, platform roadmaps, and final governance authority remain with Senior, Lead, and Architecture roles.\nCore Responsibilities:\nOwn small-to-medium ML, generative AI, and agent-enabled solutions through design, implementation, deployment, monitoring, support, performance optimization, lifecycle management, and continuous improvement.\n\nDesign and lead moderately complex production ML implementations using established architecture patterns and approved Google Cloud services, including Vertex AI and BigQuery where appropriate.\n\nOwn and enhance defined components of shared MLOps, Feature Store, model-observability, AgentOps, GenAIOps, deployment, or reusable ML-service capabilities that support multiple products and teams.\n\nDevelop modular, reusable, secure, and testable Python solutions for data preparation, feature engineering, model development, packaging, deployment, validation, monitoring, and automation.\n\nImprove MLOps pipelines, CI/CD patterns, templates, controls, and engineering accelerators using Git, peer review, automated testing, documentation, versioning, and operational standards.\n\nLead monitoring and troubleshooting for model performance, drift, reliability, data quality, and operational health; coordinate remediation, incident response, and appropriate escalation.\n\nLead model documentation, validation, and production-readiness activities while ensuring adherence to established responsible AI, legal, compliance, security, architecture, and engineering requirements.\n\nPartner with Data Engineers to design, validate, and improve trusted, reusable features and datasets, and use SQL and BigQuery for exploration, reconciliation, feature preparation, and performance analysis.\n\nBuild and evaluate models using appropriate techniques, metrics, validation, explainability, and reproducibility practices; guide less-experienced engineers in applying these practices.\n\nDesign and operationalize GenAI, retrieval-augmented generation, and agent-enabled solutions using approved enterprise models, data, tools, APIs, and guardrails.\n\nReview code, technical designs, deployment approaches, and AI-assisted outputs; provide actionable feedback and ensure alignment with team standards and maintainability expectations.\n\nMentor junior team members and interns through onboarding, technical coaching, pair development, code reviews, design reviews, and knowledge sharing.\n\nCommunicate design decisions, delivery status, production health, risks, and recommendations to technical and non-technical stakeholders; participate in agile planning and cross-functional delivery.\n\nRequired Qualifications:\nBachelor's or master’s degree in computer science, data science, engineering, information systems, or a related field, or equivalent practical experience.\n\nFive or more years of relevant experience in ML engineering, MLOps, data science, software engineering, or a closely related discipline.\n\nAdvanced Python proficiency and strong SQL proficiency for model development, production engineering, automation, validation, troubleshooting, and performance optimization.\n\nDemonstrated experience independently designing, deploying, monitoring, supporting, and improving production ML solutions.\n\nDemonstrated experience with production MLOps concepts and practices, including CI/CD, packaging, deployment, versioning, reproducibility, observability, and lifecycle management.\n\nExperience owning or materially enhancing reusable ML platform components, frameworks, services, templates, or engineering accelerators.\n\nExperience with Git-based source control, automated testing, peer code reviews, technical documentation, APIs, and modular software-engineering practices.\n\nWorking knowledge of cloud architecture and hands-on experience with cloud-based ML and data services; familiarity with BigQuery or a comparable cloud data warehouse.\n\nExperience leading technical reviews and mentoring less-experienced engineers, interns, or project contributors.\n\nStrong analytical, troubleshooting, incident-management, communication, and cross-functional collaboration skills.\n\nPreferred Qualifications:\nHands-on experience with Vertex AI or a comparable managed ML platform, including pipelines, registries, endpoints, or monitoring.\n\nExperience with reusable Feature Store capabilities, shared model-observability services, or multi-product MLOps platform components.\n\nExperience with scikit-learn, TensorFlow, PyTorch, XGBoost, or comparable ML frameworks.\n\nExperience designing or operationalizing generative AI, retrieval-augmented generation, agent frameworks, AgentOps, or GenAIOps capabilities.\n\nFamiliarity with containers and additional Google Cloud services used for serverless processing, integration, orchestration, APIs, or monitoring, such as Cloud Run.\n\nExperience supporting responsible AI, model risk, privacy, security, legal, compliance, or architecture reviews for production ML or AI solutions.\n\nTeam & Culture:\nCollaborates effectively with ML Engineers, Data Engineers, Data Scientists, Architects, Product and Domain Owners, and business partners across onshore and offshore teams.\n\nDemonstrates ownership, adaptability, accountability, sound technical judgment, and commitment to reliable production outcomes.\n\nProvides constructive technical guidance, welcomes feedback, and helps develop Engineer I resources and interns without formal people-management accountability.\n\nSupports knowledge sharing, responsible AI, reusable engineering, disciplined operations, and continuous improvement aligned with business objectives and technical standards.\n\nInfluences technical execution within defined areas of ownership while partnering with Senior, Lead, and Architecture roles on enterprise direction and roadmap 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