{"id":62523,"url":"https://alion.io/job/point-click-care-us-principal-ml-system-engineer","title":"(US) Principal ML System Engineer","company":{"id":3321,"name":"PointClickCare","domain":"pointclickcare.com","url":"https://alion.io/company/point-click-care","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":76,"open_postings":9,"ghost_share":0,"stale_share":0.778,"repost_share":0,"time_to_fill_p50_days":60,"computed_at":"2026-10-22T06:10:10Z"}},"role":"DevOps","role_family":"DevOps","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":158000,"max":198000,"currency":"USD","period":"year","gross":null,"usd_annual":198000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Java","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false}],"status":"live","first_seen_at":"2026-08-13T04:25:30Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-10-22T04:49:00Z","board_verified":true,"closed_at":null,"days_open":70,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":70},"description":"Team Summary\nThis team will serve as the product owner for the machine learning platform capabilities within PointClickCare, working closely with other engineering teams across the organization to identify, build and support traditional machine learning (ML) and hybrid ML/LLMsolutions. This centralized team with deep specialization will closely integrate with key horizontal partners to ensure delivery of safe, scalable, and high-impact AI products.\nJob Summary\nThe Principal AI Machine Learning Platform Engineer will set the technical vision and strategy for the machine learning platform that powers ML and generative AI development across PointClickCare, partnering with Product and Engineering leadership to align that direction with product and business goals. As the technical authority for the ML platform, the Principal Engineer will define the reference architectures, standards, and roadmap for the pipelines, tooling, and infrastructure used for model training, deployment, serving, and monitoring, and will provide technical leadership and mentorship to raise the engineering bar for ML systems company-wide.\nKey Responsibilities\nPartner with product and engineering leadership to translate business and product objectives into a multi-quarter technical strategy and roadmap for the ML platform.\nDefine the reference architectures and standards for scalable data and ML pipelines spanning model training, evaluation, deployment, and serving that engineering teams across the organization build upon.\nSet the direction and best practices for MLOps across the company - including CI/CD for models, model registry, feature stores, and experiment tracking - and drive build-vs-buy decisions for core platform components.\nEstablish the practices and architecture for reliability, observability, and performance of ML systems in production, including monitoring, alerting, and automated remediation.\nEstablish the security architecture for the ML platform, including authentication, role-based access control, audit logging, and compliance monitoring, and ensure adoption across teams.\nDefine secure, cost-efficient integration and infrastructure patterns for connecting the platform with existing systems, APIs, and data sources at scale.\nProvide technical leadership and mentorship across engineering teams, guiding senior engineers and influencing the org-wide technical roadmap for ML infrastructure.\nQualifications & Skills\nExpert level in Python and Java with strong software engineering fundamentals.\nDeep experience designing and building ML platforms and ML Ops workflows at scale, familiarity with tools such as MLFlow, Kubeflow, Ray, and model-serving frameworks or equivalents.\nExtensive experience with cloud platforms (AWS, Azure, and/or GCP), containerization, and orchestration (Docker, Kubernetes).\nDemonstrated track record of setting technical direction and driving org-wide technical initiatives across multiple teams.\nPreferred\nBachelor’s degree or higher in Computer Science, Machine Learning, or a related field.\nSufficient familiarity with Azure Machine Learning components, Databricks processing and serverless environments, and ML Frameworks to support strategic decision making\nDemonstrable history of leading and sustaining build out of critical cross-team systems\nExperience implementing security at scale including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring.\nExperience optimizing large model training and inference (including LLM serving) for performance and 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