Senior Machine Learning Engineer – AI/ML (Healthcare Technology) - W2

Senior Machine Learning Engineer – AI/ML (Healthcare Technology) Location: Remote (Eastern or Central US time zones preferred) Company Overview Our client is a healthcare technology innovator on a mission to optimize clinical asset performance and drive operational excellence across healthcare systems. Our platform helps hospitals and healthcare providers maximize medical equipment uptime, plan capital investments more effectively, and support clinical engineering teams with data-driven insights. We foster a collaborative, inclusive culture where cutting-edge ideas in AI and machine learning become real-world solutions that improve patient care. Job Summary We’re looking for a Senior Machine Learning Engineer who is passionate about applying AI/ML to transform healthcare operations. In this role, you will design, develop, and deploy intelligent systems that power predictive maintenance of medical devices, optimize asset lifecycles, inform smarter capital planning, and even build conversational AI assistants for clinical engineering teams. You’ll work at the intersection of AI innovation and healthcare technology, using your expertise in machine learning, data engineering, and MLOps to deliver production-grade solutions with meaningful business impact. This is a hands-on, high-impact position where you will collaborate with cross-functional teams to turn complex healthcare challenges into scalable AI solutions. If you thrive in an environment where you can drive innovation and see your models make a real difference in hospitals and clinics, we'd love to meet you! Key Responsibilities • Model Development & Deployment: Design, train, and deploy machine learning and deep learning models to support predictive analytics, equipment utilization insights, and anomaly detection use cases. Ensure models are robust and optimized for performance in a production environment. • Conversational AI & Agents: Build and fine-tune intelligent conversational agents (chatbots/assistants) for clinical support and decision-making, using Large Language Models (LLMs) and retrieval-augmented generation (RAG) pipelines to provide relevant, real-time answers. • Data Engineering & Feature Pipelines: Partner with data engineers to create feature-rich datasets using Snowflake and Azure data services. Develop reliable data pipelines and ensure data quality, lineage, and scalability for ML workloads. • MLOps & Productionization: Implement and maintain CI/CD pipelines for model training, testing, and deployment using Azure ML, MLflow, and Kubernetes. Automate model monitoring, drift detection, and retraining workflows to keep models accurate and up-to-date. • AI Integration & APIs: Integrate ML models into core products and workflows (e.g. clinical asset management systems, real-time location system (RTLS) dashboards, capital planning tools) via robust APIs or serverless frameworks. Work closely with software teams to embed AI capabilities seamlessly into the user experience. • Innovation & Research: Stay at the forefront of AI by evaluating and prototyping emerging technologies (Azure OpenAI services, Snowflake Cortex AI, vector databases, etc.). Recommend how new tools or techniques can enhance our AI offerings and drive greater value for our clients. • Cross-Functional Collaboration: Work closely with product managers, domain experts, and software developers to translate business challenges into AI solutions. Communicate findings and progress to stakeholders, and mentor junior engineers in best practices for scalable machine learning. Qualifications • Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. (Ph.D. in a relevant discipline is a plus.) • Experience: 6–10+ years of hands-on experience in applied machine learning engineering. Proven track record of developing and deploying production-grade models in an enterprise setting (healthcare industry experience is a bonus). • Technical Skills: Proficiency in Python and SQL is required (experience with Scala or Java is a plus). Strong background in ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers. Experience with cloud platforms and big data tools, especially Azure ML ecosystem (Azure ML, Data Factory, Synapse, or Databricks) and data warehouses like Snowflake (preferred), BigQuery, or Redshift. Familiarity with MLOps and containerization tools – e.g. MLflow, Kubeflow, Docker, Kubernetes – and infrastructure-as-code (Terraform) for deploying ML services. • AI/ML Expertise: Deep understanding of machine learning algorithms, statistical modeling, and feature engineering. Ability to build and refine models for classification, regression, time-series forecasting, and anomaly detection. Knowledge of model governance, versioning, and best practices for validation and A/B testing. • Conversational AI: Experience working with NLP and conversational AI is highly desired. Knowledge of building chatbots or virtual assistants using frameworks like Azure OpenAI, LangChain, or similar, and understanding of how to implement RAG (Retrieval-Augmented Generation) architectures. • Collaboration & Communication: Excellent problem-solving skills with the ability to explain complex AI concepts to non-technical stakeholders. Experience collaborating in agile teams and a passion for mentoring others in the adoption of AI/ML solutions. • Work Authorization: Must be currently authorized to work in the United States for any employer (W2 employment only – we are unable to sponsor visas or work with C2C arrangements). Nice-to-Have Skills While not required, any of the following will help you stand out: • Experience working with clinical engineering data, medical device telemetry, or RTLS analytics in a healthcare environment. • Knowledge of AI safety, bias mitigation, and explainable AI frameworks to ensure models are fair and transparent. • Familiarity with Snowflake Cortex or Azure Cognitive Services for building AI solutions. • Understanding of healthcare industry regulations and standards (e.g. HIPAA, FDA software validation, ISO 13485) and how they apply to AI solutions. What Success Looks Like Within the first year in this role, success will mean: • Impactful AI Solutions: You have developed and deployed AI models (including conversational assistants) into production that are seamlessly integrated into our products, leading to improved clinical workflows, equipment reliability, and cost efficiencies for our customers. • Robust ML Pipelines: You have established automated, scalable pipelines for continuous model training, evaluation, and monitoring. Model performance is consistently tracked, and any drifts are detected and addressed with minimal downtime. • Data-Driven Decisions: Clinical engineering and operations teams are leveraging the insights from your models for smarter capital planning and asset management – e.g., using predictive analytics to schedule maintenance or allocate resources, resulting in measurable improvements in uptime and cost savings. • Advancing AI Maturity: You have contributed significantly to our AI/ML roadmap, perhaps introducing new technologies or best practices. Your work has led to tangible ROI (return on investment) for AI initiatives, helping the company achieve multi-million dollar gains or savings through AI-driven products. • Team Collaboration: You’ve become a go-to AI expert within the company – working closely with cross-functional teams and maybe even mentoring junior data scientists/engineers – and have helped elevate the overall data science practice.