Principal Software Engineer - Real-Time ML Systems on GCP

Hispanic Alliance for Career Enhancement

Raleigh (NC)

On-site

USD 144,200 - 288,400

Full time

14 days+

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Job summary

CVS Health is seeking a Principal Software Engineer to lead hands-on development of a real-time clinical decision-support platform on Google Cloud. You will design, implement, and ship high‑performance components, including LLM pipelines and production ML workflows, while mentoring engineers across UI, backend, and ML.

You will own the architecture of distributed services with strict SLOs, build scalable APIs, and ensure reliability and security.

Qualifications

10+ years of software engineering experience delivering production systems at scale.
5+ years as hands-on Staff or Principal-level engineer coding, not removed from implementation.
Expert proficiency in Python including performance tuning, concurrency, and system optimization.
5+ years building real-time distributed systems with latency and reliability requirements.
5+ years working in a major public cloud (GCP preferred: Vertex AI, GKE, Cloud Run, Pub/Sub, BigQuery, IAM).
Deep experience with microservices architecture, API design (REST), and resilient systems.
3+ years building and operating production ML systems (MLOps, monitoring, retraining, deployment pipelines).
3+ years developing LLM-powered applications in production, including prompt design, RAG/vector search, evaluation frameworks, and performance optimization.
Experience building modern UI components with React or Angular.
Strong computer science fundamentals, including distributed systems, algorithms, and concurrency.

Responsibilities

Own architecture and execution of a real-time, multi-service clinical platform on GCP and personally build critical components.
Build production-grade LLM pipelines with prompt engineering, evaluation frameworks, and vector search.
Deliver real-time, high-performance distributed services with sub-second clinician experiences and strong fault tolerance.
Operationalize ML through Vertex AI deployment, feature pipelines, drift detection, and CI/CD quality controls.
Design flexible APIs and microservices with robust testing, observability, and progressive delivery practices.
Raise engineering bar with code reviews, testing standards, security, and observability across teams.
Own outcomes end-to-end by translating clinical needs into delivered solutions and monitoring real-world performance.
Collaborate with Data Science, Product, Clinical SMEs, AI Governance, and Security for enterprise compliance.