{"id":181990,"url":"https://alion.io/job/abbott-ai-platform-engineer","title":"AI Platform Engineer","company":{"id":2710,"name":"Abbott","domain":"abbott.com","url":"https://alion.io/company/abbott","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":98,"open_postings":147,"ghost_share":0.007,"stale_share":0.252,"repost_share":0.048,"time_to_fill_p50_days":15,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Madison, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":61300,"max":122700,"currency":"USD","period":"year","gross":null,"usd_annual":122700},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GitOps","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Argo Workflows","optional":true},{"name":"Feature Store","optional":true},{"name":"Go","optional":true},{"name":"HIPAA","optional":true},{"name":"Java","optional":true},{"name":"KEDA","optional":true},{"name":"Knative","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"Progressive Delivery","optional":true},{"name":"Quantization","optional":true},{"name":"SOC 2","optional":true},{"name":"TensorRT","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-08-31T00:00:00Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-10-17T06:39:34Z","board_verified":true,"closed_at":null,"days_open":47,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":47},"description":"Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries.JOB DESCRIPTION:\nPosition Overview\nThe AI Platform Engineer builds and operates the machine learning and generative AI platform used by teams across Abbott Cancer Diagnostics. You'll own the full model lifecycle in production - data and feature pipelines, training and experimentation, evaluation and promotion, serving, and monitoring - along with the platform services, compute and tooling underneath it. This is hands-on infrastructure work backed by solid platform engineering practice: making inference fast and cheap, making the path from experiment to production repeatable and auditable, and shipping interfaces other engineers can build on - in support of software that ultimately reaches patients.\nEssential Duties\nInclude, but are not limited to, the following:\nBuild and maintain data, feature, and training pipelines for ML and LLM workloads - ingestion, transformation, fine-tuning, distributed training, and reproducible experiment execution with lineage tracked from dataset and code to resulting model.\n\nImplement automated evaluation and promotion gates - performance benchmarks, regression checks, and validation criteria that determine whether a model advances toward production.\n\nAutomate the model lifecycle end to end through CI/CD and GitOps: packaging, promotion across environments, progressive rollout, and rollback.\n\nBuild and operate production model-serving infrastructure for LLMs and predictive models, including inference optimization, autoscaling, and low-latency serving across multiple model formats and runtimes.\n\nArchitect and manage GPU infrastructure - scheduling, autoscaling, resource isolation, and utilization efficiency for training and inference workloads.\n\nInstrument the platform and the models on it - structured logging, telemetry, drift detection, and cost tracking - and build the triggers and pipelines that close the loop into retraining and revalidation.\n\nExtend model, dataset, and artifact registries, metadata systems, and versioning so every deployed model has a traceable, auditable history.\n\nBuild the developer-facing surface of the platform: APIs, SDK components, templates, documentation, and runbooks that make it self-service, backed by well-tested code and active participation in design and code review.\n\nUphold company mission and values through accountability, innovation, integrity, quality, and teamwork.\n\nMaintain regular and reliable attendance.\n\nAct with an inclusion mindset and model these behaviors for the organization.\n\nMinimum Qualifications\nBachelor's degree in Computer Science, Engineering, AI/ML, or a related field; or equivalent practical experience.\n\n3+ years building and operating production software, including significant work on ML or AI infrastructure.\n\nStrong Python, including software engineering fundamentals - automated testing, code review, and designing code others will read and extend.\n\nExperience with the ML model lifecycle: pipelines that carry a model from training through evaluation, deployment, monitoring, and retraining.\n\nKubernetes experience - deploying, scaling, and debugging containerized workloads on a major cloud provider (AWS preferred).\n\nExperience with CI/CD, GitOps-based delivery, and infrastructure automation.\n\nPreferred Qualifications\nML workflow orchestration and experiment tracking (for example Kubeflow, Argo Workflows, Airflow, MLflow, or Weights & Biases).\n\nGPU infrastructure at scale: scheduling, multi-tenancy and resource isolation, autoscaling, and inference optimization such as quantization, batching, or graph compilation (for example ONNX Runtime or TensorRT).\n\nFeature stores, data versioning, or dataset lineage tooling.\n\nProgressive delivery for models - canary, shadow, or A/B deployment and automated rollback.\n\nModel monitoring and drift detection, including automated retraining triggers.\n\nModel governance and reproducibility practices: lineage tracking, audit trails, and approval workflows.\n\nKubernetes-native serverless and event-driven autoscaling (for example Knative or KEDA).\n\nExperience building APIs or shared libraries consumed by other engineering teams.\n\nProduction experience in an additional systems language such as Go or Java.\n\nFine-tuning foundation models, or building distributed training pipelines.\n\nExperience delivering software in a regulated environment (HIPAA, CLIA, GxP, SOC 2).\n\nWe are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, creed, disability, gender identity, national origin, protected veteran status, race, religion, sex, sexual orientation, and any other status protected by applicable local, state, or federal law. Applicable portions of the Company’s affirmative action program are available to any applicant or employee for inspection upon request.\nThe base pay for this position is\n$61,300.00 - $122,700.00In specific locations, the pay range may vary from the range posted.\nJOB FAMILY:\nProduct DevelopmentDIVISION:\nONCO Cancer DiagnosticsLOCATION:\nUnited States > Madison : 5505 Endeavor LnADDITIONAL LOCATIONS:\nWORK SHIFT:\nStandardTRAVEL:\nYes, 10 % of the TimeMEDICAL SURVEILLANCE:\nNoSIGNIFICANT WORK ACTIVITIES:\nContinuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day), Keyboard use (greater or equal to 50% of the workday)Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdfEEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf","description_format":"text","description_chars":5985,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Prescription Drugs","Cardiovascular Devices","Diabetes Devices","Neurotech & Neuromodulation"],"lifecycle":[{"event":"open","at":"2026-09-01T00:19:17Z"},{"event":"close","at":"2026-09-22T21:18:51Z"},{"event":"reopen","at":"2026-09-23T23:25:26Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"green card filings: 1","filings_12m":0,"filings_prev_12m":0,"green_card_filings_12m":1,"median_offered_wage_usd":112500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":0}],"liveness":{"score":12,"band":"cold","label":"Long shot","p_open":1,"p_active":0.441,"p_room":0.28,"age_days":47,"expected_fill_days":15,"reasons":["conf:3","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-17T05:46:00Z"},"pay":{"stated_usd_annual":122700,"is_top_pay":false},"html_url":"https://alion.io/job/abbott-ai-platform-engineer","json_url":"https://alion.io/job/abbott-ai-platform-engineer.json","meta":{"generated_at":"2026-10-17T11:32:02Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":15098,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2710},"rest":"https://alion.io/mcp/rest/get_company?id=2710"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fabbott-ai-platform-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fabbott-ai-platform-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fabbott-ai-platform-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/abbott-ai-platform-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fabbott-ai-platform-engineer"}]}