GCP AI Engineer - Cambridge, MA

Lumeris•Massachusetts, MA

1d•$143,190 - $194,468•Hybrid

About The Position

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.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, AI, Health Informatics, or related field.
4+ years of experience building or supporting production software or AI/ML systems, including meaningful exposure to Google Cloud.
Experience with Python and modern AI/ML frameworks.
Familiarity with containerized deployments (Docker, Kubernetes) and cloud-native systems.
Experience contributing to or supporting ML pipelines, model monitoring, or AI-enabled services.
Interest in or exposure to healthcare data or regulated environments.

Nice To Haves

Experience with Vertex AI, Kubeflow, or GCP-based ML workflows.
Exposure to FHIR, HL7, or EMR systems (including EPIC).
Experience building agentic AI, RAG systems, or conversational AI.
Background in MLOps, model monitoring, or AI operations.
Google Cloud certifications (AI Engineer, DevOps, Cloud Architect).

Responsibilities

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.
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.
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.
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.
Build, test, and deploy models supporting: Clinical decision support, Patient and clinician interaction, Workflow automation.
Leverage Vertex AI, BigQuery, Dataflow, and Looker for scalable analytics and deployment.
Use GCP Cloud Operations (Stackdriver) for monitoring, alerting, and troubleshooting.
Rapidly diagnose and resolve production issues in distributed AI systems.
Continuously optimize for performance, cost, and reliability.
Apply best practices for IAM, VPC configuration, encryption, and secure access.
Ensure compliance with HIPAA and healthcare data privacy standards.
Collaborate closely with clinical, product, and IT teams to translate complex needs into working AI solutions.
Provide documentation, knowledge sharing, and hands on support for deployed systems.
Stay current with advances in GCP, agentic AI, and generative AI.
Prototype, test, and validate new AI workflows, moving successful ideas into production.