Machine Learning Engineer

The Opportunity Newfire Global Partners is seeking a Machine Learning Engineer for a high-impact engagement with a national healthcare organization. You will build the production systems that move machine learning and AI from experimentation into secure, reliable use across clinical and operational workflows. This role sits at the intersection of data science, software engineering and cloud platforms. You will develop reusable pipelines and inference services, improve the machine learning platform and make sure models remain observable, scalable, governed and cost-effective in production. What You Will Do Production Machine Learning - Own the engineering lifecycle from technical design and model handoff through deployment, monitoring, retraining, rollback, retirement and incident response. - Build reusable batch, streaming and real-time pipelines for training and inference. - Develop containerized model services and APIs with clear contracts, versioning, resilience and rollback patterns. - Integrate model outputs into healthcare products and clinical or operational workflows. Platform Engineering and MLOps - Build automated CI/CD and infrastructure-as-code workflows for packaging, validation, security scanning and deployment across environments. - Use Python, SQL, PySpark, Delta Lake, MLflow and Unity Catalog within Azure and Databricks environments. - Implement model registration, approval, lineage, reproducibility, access control and auditability. - Improve developer experience through shared libraries, templates, automated testing, documentation and code review. - Support capacity planning, performance tuning and cost-aware cloud architecture. Reliability and Observability - Define controls for data quality, feature availability, latency, service reliability, model performance, drift, bias and cost. - Establish service objectives, alerts, runbooks, escalation paths and production troubleshooting practices. - Create safe processes for retraining, environment promotion, rollback and incident response. Healthcare Data and Governance - Engineer dependable feature and training datasets from claims, EHR, laboratory and care operations data. - Build controls for provenance, schema changes, freshness, data leakage and training-serving consistency. - Protect health information through secure engineering practices appropriate for HIPAA-regulated environments and HITRUST-aligned controls. - Support models that may influence clinical or patient-facing decisions with appropriate documentation and governance. - Implement technical controls supporting clinical validation, human oversight, auditability, and safe-failure mechanisms for AI solutions influencing patient care. Applied Generative AI - Evaluate and operationalize foundation models, retrieval-augmented generation, vector search, model gateways and agentic workflows when appropriate. - Apply sound judgment about where generative AI adds value and where simpler approaches provide better reliability or economics. - Implement evaluation, security, quality and supportability requirements before emerging capabilities reach production. Technical Partnership - Convert research code into modular, tested and maintainable production software in partnership with data scientists. - Establish engineering acceptance criteria and challenge designs that will not scale or operate safely. - Translate ambiguous clinical or business needs into measurable engineering requirements. - Explain architecture choices, delivery risks and production performance to technical and nontechnical stakeholders. What Success Looks Like - Machine learning solutions move from experimentation to production through a repeatable engineering path. - Production services remain reliable, observable, secure and cost-conscious. - Data scientists can move faster because common platform capabilities and deployment patterns already exist. - Models and AI services can be reproduced, governed, monitored and safely retired. - Production performance is measured and continuously improved through KPIs for service reliability, inference latency, model quality, data freshness, and operating cost. Skills, Knowledge & Expertise Required Qualifications - Bachelor's degree in computer science, software engineering, data science, statistics or a related quantitative field, or equivalent practical experience. - At least three years of experience building and operating production software, data or machine learning systems. - At least two years of experience deploying and supporting machine learning models in production. - Strong Python and SQL skills, plus experience with PySpark and distributed data processing. - Demonstrated ability to write modular, tested and maintainable software. - Hands-on experience with Azure and Databricks or comparable cloud and lakehouse platforms. - Experience serving models through scheduled batch workloads, streaming pipelines or real-time APIs using containers and orchestration tools. - Experience with Git, code review, automated testing, CI/CD, infrastructure as code, secrets management and environment promotion. - Working knowledge of data and model observability, including lineage, drift, monitoring, retraining triggers, rollback and troubleshooting. - Understanding of feature engineering, evaluation, reproducibility, data leakage and training-serving skew. - Ability to explain architecture, risk and tradeoffs to technical and nontechnical partners. Preferred Qualifications - Master's degree in a relevant technical or quantitative field. - Experience with MLflow, Unity Catalog, Delta Lake, Azure Machine Learning, Azure Kubernetes Service or Azure Container Apps. - Experience with healthcare claims, EHR, FHIR/HL7, laboratory or care operations data. - Knowledge of HIPAA requirements and HITRUST-aligned controls. - Experience operationalizing generative AI, retrieval-augmented generation, vector search or agentic workflows. About Newfire Global Partners Newfire Global Partners is a leading custom software development company that specializes in working on complex problems. With a strong focus on meeting the unique needs of each organization, they connect clients with local leaders and trusted remote teams to deliver exceptional IT services and software development solutions. Their range of expertise spans various domains, including Data & Analytics, AI/ML, Software Engineering, Product Management, and Marketing. By leveraging recent innovations in artificial intelligence, Newfire Global Partners ensures that their clients can transform the new normal into a competitive advantage. Through their advisory services, experienced leaders guide businesses through transformations and de-risk operations throughout the entire lifecycle. They also offer talent augmentation, bringing together highly skilled professionals from multi-disciplinary teams to support software, product, and marketing needs. Newfire Global Partners is committed to providing excellent service and driving client growth. Their team of industry experts works closely with organizations, whether it's driving transformation, building from scratch, or getting things done efficiently. Trusted by VC & PE firms, established businesses, and startups, Newfire Global Partners is proud to be a reliable partner in various industries, including healthcare, enterprise, and fintech. Partner with Newfire Global Partners today and unlock the expertise that accelerates success.