GCP AI Engineer - Cambridge, MA

Your Future is our FutureAt Lumeris, we believe that our greatest achievements are made possible by the talent and commitment of our team members. That's why we are actively seeking talented and collaborative individuals who are passionate about making a difference in the healthcare industry. Join us today as we strive to create a system of care that every doctor wants for their own family and become part of a community that values its people and empowers you to make an impact.We're excited to consider every qualified candidate authorized to work in the United States, although we are unable to sponsor visas for this role at this time.Position:GCP AI Engineer - Cambridge, MAPosition Summary:Lead the design, development, and deployment of AI solutions on Google Cloud that elevate patient care and streamline healthcare operations. This role is for engineers who ship end-to-end. You will own problems from definition through production—using AI as a core part of your workflow, not an occasional tool. Your work spans data engineering, model building, and AI Ops, delivering intelligent, production-ready healthcare applications and agents used by clinicians, care teams, and patients.Job Description:Key Responsibilities End to End Ownership Own features from problem framing through production deployment and iteration. Work with clinical, product, and engineering partners to define the right problem before building the solution. Stay accountable for outcomes after launch, including performance, reliability, and usability in real-world healthcare settings. HandsOn Agentic & Generative AI Development Build, troubleshoot, and optimize agentic AI systems using Python, LangChain, LangGraph, and Gemini APIs on Google Cloud. Embed AI agents directly into clinical workflows and user-facing applications, not just prototypes. Design and deploy RAG-based and conversational AI systems that are accurate, grounded, and trustworthy. AI Ops / ML Ops Implementation Design and automate end to end ML pipelines covering training, validation, deployment, monitoring, and updates. Use Vertex AI, Kubeflow, Cloud Build, Terraform, and related tooling to ensure models are reproducible, observable, and reliable. Monitor production systems, detect drift, and iterate—treating “merge” as the beginning, not the end. Healthcare Data Engineering Construct secure, compliant data pipelines integrating EHR, FHIR, and HL7 data formats. Support interoperability with EMR systems such as EPIC. Implement validation and quality checks appropriate for regulated healthcare environments. Develop & Deploy AI/ML Models Build, test, and deploy models supporting:  Clinical decision support Patient and clinician interaction Workflow automation Leverage Vertex AI, BigQuery, Dataflow, and Looker for scalable a...