Sr Full Stack AI Engineer

About the role

This is a high-autonomy, high-ownership role on a small, senior engineering team with minimal bureaucracy. AI-assisted software development is a core engineering competency and will be evaluated throughout the interview process.

Key Responsibilities

• Design, build, and enhance the enterprise AI platform, including model and agent lifecycle management, AI control-plane services, developer tooling, runtime orchestration, memory services, workflow management, and governance capabilities.
• Build production-ready agentic AI solutions that solve complex business problems using multi-agent architectures, structured planning, tool integration, retrieval, memory, and human-in-the-loop workflows.
• Develop secure, cloud-native AI infrastructure using Google Cloud Platform, Kubernetes, Infrastructure as Code, CI/CD, observability, and enterprise identity and access management while maintaining portability through open standards.
• Implement MLOps and LLMOps capabilities, including model deployment, evaluation, observability, monitoring, cost optimization, runtime governance, testing, and safe release practices for production AI systems.
• Partner with security, architecture, legal, and risk teams to embed responsible AI, governance, security, and compliance into platform capabilities and enterprise AI solutions.
• Build platform capabilities as intelligent agents wherever appropriate, enabling the platform to automate lifecycle management, planning, governance, and operational workflows.
• Leverage AI-assisted engineering throughout the software development lifecycle to accelerate delivery while maintaining high standards for quality, security, and reliability.

Requirements

• Strong software engineering experience with Python and experience in one or more additional languages such as TypeScript or Java.
• Hands-on experience with modern generative AI technologies, including LLMs, multi-agent orchestration, retrieval-augmented generation (RAG), memory architectures, tool integration, and evaluation frameworks.
• Experience with AI platform engineering, including model lifecycle management, agent runtimes, observability, developer tooling, and enterprise integration patterns.
• Strong cloud engineering experience, preferably with Google Cloud Platform, including managed AI services, Kubernetes, networking, identity, containers, CI/CD, and Infrastructure as Code.
• Experience implementing MLOps and LLMOps practices, including model deployment, evaluation, monitoring, tracing, performance optimization, and production operations.
• Experience using AI-assisted software development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or similar technologies.
• Strong communication and collaboration skills with the ability to work effectively across engineering, architecture, security, and business teams.
• Able to operate successfully within a regulated enterprise environment while balancing innovation with governance.

Preferred Qualifications

• Experience within financial services, insurance, healthcare, or another highly regulated industry.
• Experience with Vertex AI, LangChain, Google ADK, CrewAI, AutoGen, MLflow, OpenTelemetry, GraphRAG, Ray, vLLM, or related AI platform technologies.
• Knowledge of enterprise AI governance frameworks including NIST AI RMF, ISO 42001, SOC 2, HIPAA, GDPR, or emerging AI regulations.
• Experience building reusable developer platforms or internal engineering frameworks adopted across multiple teams.