Full-Stack AI Engineer

Juno Beach, United States | Posted on 10/13/2026

Our teams create full-stack products across established and emerging technologies, choosing the right tools to solve real client challenges. You could be part of:

• Creating the digital experiences people use every day by designing and building modern web and mobile applications with technologies like React, Angular, and Vue.
• Powering business-critical systems at scale through APIs and backend services that enable innovation, performance, and reliability using technologies such as Python, Node.js, and Type Script.
• Bringing the next generation of AI-powered solutions to life by leveraging cloud platforms and Generative AI services from AWS and Google Cloud to solve complex business problems.
• Driving efficiency and accelerating delivery through automated CI/CD pipelines, modern infrastructure practices, and operational improvements that help teams move faster and smarter.
• Shaping the future of software development by collaborating in agile teams that embrace Generative AI tools and modern engineering practices to deliver meaningful outcomes for clients including Claude Code, Git Hub Copilot, Cursor, Claude Code, OpenAI Codex, Kiro, DPaul Nyaga – Principal Software Engineer / AI & ML Engineering
• Senior Python / Software Engineering: Brings 10+ years building production Python, ML, and backend systems, with experience spanning application development, APIs, distributed services, performance optimization, and production deployment.
• Production AI / LLM
  
  Experience:
  
  Strong current hands-on experience with agentic AI, RAG, Lang Graph/Lang Chain, OpenAI APIs, vector search, MCP/A2A agent protocols, evaluation, and guardrails. He has built production AI applications rather than only experimental or proof-of-concept solutions.
• Agentic AI & Quality Controls: Particularly well aligned with the role's AI-assisted development requirements. Paul has implemented bounded agent execution, attempt limits, timeouts, typed evidence contracts, deterministic tool calls, grounding checks, structured-output validation, and regression testing for LLM/RAG changes.
• Architecture / Full Lifecycle Ownership: Has designed FastAPI service boundaries separating deterministic application services from model-driven workflows and has built systems from architecture through deployment, monitoring, and production support.
• Cloud / Dev Ops: Hands-on experience across GCP and Azure with Docker, Kubernetes, CI/CD/MLOps, distributed systems, and production infrastructure. His current AI services are containerized and deployed using GCE/GKE and Kubernetes.
• Reliability & Observability: Strong production engineering background with Lang Smith, Logfire, structured logging, tracing, performance monitoring, validation, and debugging across distributed AI services.
• Applied ML Engineering: Prior experience includes production forecasting, search/ranking systems, anomaly detection, optimization services, and enterprise AI-assisted migration tools. At GM, he developed an AI-assisted SQL migration solution combining LLM generation, RAG, deterministic validation, and human review.
• Broad Engineering Foundation: His background extends beyond GenAI into traditional software engineering and ML, including Python, C#, C++, SQL, APIs, microservices, cloud infrastructure, and performance optimization. He has been developing production software and technical systems since his earlier engineering work at Schlumberger.

Overall Fit: Paul is a strong match for the Principal Software Engineer role, particularly across Python, production AI/LLM development, agentic architectures, RAG, cloud, Kubernetes, CI/CD, observability, and AI validation/guardrails
. His recent work maps particularly well to the position's emphasis on…