GCP AI Engineer - Boston, MA

Lumeris•Boston, MA

1d•Hybrid

About The Position

At 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. Position Summary: Lead the design, development, and deployment of AI solutions on Google Cloud that elevate patient care and streamline healthcare operations. Your work will span data engineering, model building, and AI Ops—delivering intelligent, production-ready healthcare applications and agents.

Requirements

Bachelor’s or Master’s in Computer Science, Engineering, Health Informatics, AI, or similar.
4+ years of hands-on engineering experience developing and deploying AI/ML solutions on GCP, with direct experience in clinical/healthcare environments.
Demonstrated expertise with MLOps and AI Ops (Vertex AI, Kubeflow, MLflow, CI/CD, monitoring, and workflow orchestration tools).
Proven programming skills in Python, plus experience with Docker, Kubernetes, cloud automation, and Terraform.
Direct experience creating agentic AI applications, troubleshooting APIs (Gemini, agent development kits), and deploying healthcare agents and RAG applications.
Familiarity with clinical data standards (FHIR, HL7), EMR systems (EPIC), and best practices for data security and governance in health tech.
Ability to commute to Cambridge office 3 times per week.

Nice To Haves

Google Cloud Professional certifications (AI Engineer, DevOps, Cloud Architect, Data Engineer) highly desirable.
Experience with operationalizing GenAI for healthcare (conversational agents, clinical summarization, documentation automation).
Skill in data processing, workflow orchestration (Cloud Composer), and error troubleshooting for healthcare pipelines.
Proven ability integrating trust frameworks, AI validation, and compliance methods into healthcare and agentic applications.
Strong stakeholder communication, technical support, and documentation abilities.

Responsibilities

Hands-On Agent Development: Build, troubleshoot, and optimize agentic AI applications—using frameworks such as LangChain and LangGraph, Python, and Gemini APIs on Google Cloud, directly embedding agents into clinical workflows and patient-facing apps.
AI Ops (MLOps) Implementation: Design and automate MLOps pipelines for model lifecycle management (training, validation, deployment, monitoring, and updates) utilizing GCP tools (Vertex AI Pipeline, Kubeflow, Cloud Build, Terraform). Ensure reproducibility, traceability, and reliability in live environments.
Data Engineering for Healthcare: Construct secure, compliant data pipelines integrating multiple health data formats (EHR, FHIR, HL7), focusing on clinical data processing, validation engines, and interoperability with EMR systems like EPIC.
Develop & Deploy AI/ML Models: Build, test, and deploy robust AI models—focused on health tech applications like clinical decision support, patient interaction, and workflow automation, using Vertex AI, BigQuery, Dataflow, and Looker.
Operational Reliability: Use GCP’s Cloud Operations Suite (Stackdriver) and custom health-tech metrics for continuous monitoring, error troubleshooting, and distributed system reliability. Rapidly diagnose and resolve production issues.
Security & Compliance: Apply best practices in privacy, IAM, VPC, and encryption for health data. Enforce regulatory compliance (HIPAA, GDPR) within all engineering work.
Collaboration & Support: Work closely with clinical, product, and IT teams; provide hands-on technical support and documentation for operationalized systems in healthcare environments.
Continuous Learning & Prototyping: Stay current with new GCP, agentic, and GenAI advances; contribute to prototypes, validation methods (including Turing Test compliance), and deployment of secure agent workflows.