Software Development Engineer; GCP, AI and Data Pipelines

We're building a world of health around every individual - shaping a more connected, convenient and compassionate health experience. At CVS Health®, you'll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger - helping to simplify health care one person, one family and one community at a time.

This role leads the architecture and delivery of advanced data and AI solutions, specializing in GCP data pipelines, MCP server setup, and agentic AI. The engineer is responsible for technical vision, hands‑on implementation, and embedding AI into engineering best practices.

Architect, design, and implement robust, scalable data pipelines on GCP using services such as Big Query, Dataflow, Pub/Sub, and Vertex AI. Ensure data pipelines are optimized for performance, reliability, and security.

Lead the setup and configuration of MCP (Model Context Protocol or Manage, Control, and Plan) servers to standardize how AI systems access tools, data, and context. Ensure MCP servers provide a universal protocol for AI agents, enabling modularity and ease of integration with new models and tools. Implement best practices for database interaction, image management, access controls, and automation integration with orchestration frameworks.

Integrate agentic AI and automation frameworks into data pipelines and engineering workflows. Enable autonomous, intelligent data processing and analytics by leveraging LLMs and agentic orchestration.

AI‑Driven Code Quality & Automation: Leverage AI‑powered tools for code generation, code review, and automated testing to improve code quality and accelerate development cycles. Integrate AI agents into CI/CD pipelines for intelligent build, test, and deployment orchestration. Use MCP servers to standardize access to codebases, data, and documentation, enabling modular and reusable AI integrations.

Data Governance & Security: Implement robust data governance policies, including data lineage, access controls, and audit trails for all AI‑driven data pipelines. Ensure all AI models and data flows comply with enterprise security standards and privacy regulations. Use MCP server features for secure, auditable, and policy‑driven access to data and tools.

Responsible & Ethical AI: Apply organizational AI governance frameworks to ensure fairness, transparency, and explainability in all AI‑powered solutions. Conduct regular fairness assessments and bias detection for AI models, leveraging available toolkits and governance processes. Document AI decision logic and maintain traceability for all automated actions.

Operational Excellence & Monitoring: Implement telemetry and monitoring for all AI and data pipeline components, including model performance, data freshness, and system health. Use automated alerting and self‑healing mechanisms where possible, leveraging AI for anomaly detection and root cause analysis. Continuously evaluate and optimize AI models and pipelines for efficiency and reliability.

Knowledge Sharing: Actively participate in the AI engineering community of practice to share best practices, lessons learned, and reusable assets. Mentor team members on AI integration patterns, responsible AI use, and advanced GCP/MCP techniques. Maintain comprehensive documentation for all AI integrations, including architecture diagrams, data flows, and operational runbooks.

Mentor and coach engineers at all levels, foster a culture of innovation and continuous learning, and collaborate with cross‑functional stakeholders to align technical solutions with strategic business goals.

• 7+ years of experience in software engineering, with significant experience in cloud‑native data pipeline development and AI/ML.
• Advanced expertise in GCP services, infrastructure, and automation.
• Hands‑on experience with MCP server setup, database automation, and agentic…