Senior Applied ML Engineer, Technology and Digital, FT,...

Baptist Health is the region's largest not-for-profit healthcare organization, with 12 hospitals, over 28,000 employees, 4,500 physicians and 200 outpatient centers, urgent care facilities and physician practices across Miami-Dade, Monroe, Broward and Palm Beach counties. With internationally renowned centers of excellence in cancer, cardiovascular care, orthopedics and sports medicine, and neurosciences, Baptist Health is supported by philanthropy and driven by its faith-based mission of medical excellence. For 25 years, we've been named one of Fortune's 100 Best Companies to Work For, and in the 2024-2025 U.S. News & World Report Best Hospital Rankings, Baptist Health was the most awarded healthcare system in South Florida, earning 45 high-performing honors.Read on to find out what you will need to succeed in this position, including skills, qualifications, and experience.What truly sets us apart is our people. At Baptist Health, we create personal connections with our colleagues that go beyond the workplace, and we form meaningful relationships with patients and their families that extend beyond delivering care. Many of us have walked in our patients' shoes ourselves and that shared experience fuels out commitment to compassion and quality. Our culture is rooted in purpose, and every team member plays a part in making a positive impact - because when it comes to caring for people, we're all in.Description:Build and integrate end to end lifecycles of large-scale, distributed machine learning systems using the latest public cloud & open source technologies. Train, evaluate, and debug machine learning models for complex tasks. Develop tools and services for improving ML systems reliability & accuracy beyond modeling choices "” model ops. Collaborate with data engineers to solve complex data problems at scale. Lead technical projects to completion. Collaborate with Data scientists & analysts. Contribute to a team culture that values engineering excellence, continuous improvement and innovation. Estimated salary range for this position is $129932.84 - $168912.69 / year depending on experience.Qualifications:Degrees:Bachelors.Additional Qualifications:Bachelors, Masters, or PhD Degree in Computer Science/Machine Learning or equivalent professional experience.Strong background in machine learning and artificial intelligence with expertise in one or more of: computer vision, NLP, speech, optimization, deep learning, reinforcement learning, time series, generative models, signals, and distributed systems.Proficiency in ML modeling frameworks.Strong overall software development approach.Significant experience building end to end data systems.Strong software engineering skills with proven experience crafting, prototyping, and delivering advanced algorithmic solutions.Proficiency in one or multiple machine learning languages (ex: Python) & development environments such as AWS Sagemaker.Role SummaryLead AI Implementation: Drive the end-to-end development of production-grade AI solutions, from LLM orchestration and backend APIs to interactive UI prototypes and automated deployment pipelines.Full-Stack Ownership: Take accountability for the technical lifecycle of AI products, ensuring they are scalable, secure, and seamlessly integrated into healthcare workflows.Essential Job FunctionsGenAI & Advanced Modeling: Develop and deploy advanced Generative AI applications using RAG patterns and model fine-tuning; architect orchestration layers and agentic workflows to ensure vendor-agnostic, autonomous problem-solving.Full-Stack Development & Prototyping: Build robust Python-based backends and scalable APIs; create interactive user interfaces (POCs) to visualize AI reasoning and gather clinical stakeholder feedback.Data & Infrastructure Integration: Integrate AI solutions with cloud data warehouses (e.g., Snowflake) and manage containerized deployments (Docker) via automated CI/CD and GitOps pipelines (GitLab, ArgoCD) on GCP.Governance, Security, & Monitoring: Engineer automated guardrails for PII/PHI masking and risk mitigation; implement observability tools to monitor model drift, hallucination rates, and token-based cost metrics (FinOps). xijylhu Safety & Interoperability: Validate clinical logic using advanced evaluation frameworks (e.g., RAGAS) and ensure seamless EHR integration through healthcare data standards like FHIR and HL7.Future-Ready Engineering: Architect multimodal systems capable of processing diverse data types (imaging, labs, and notes) to stay ahead of emerging healthcare AI trends.Minimum Required Experience: 5 YearsEOE, including disability/vets

Created: 2026-02-16