Full-Stack AI Engineer

Build solutions that matter. 

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 TypeScript.
  
• 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, GitHub 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, LangGraph/LangChain, 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 / DevOps: 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 LangSmith, 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 using AI critically while maintaining deterministic controls and production reliability.

Areas to Validate: I would confirm the depth of his modern frontend experience and examples of formal technical leadership, mentoring, and executive-level stakeholder communication. His resume is strongest on hands-on AI/ML and backend engineering rather than traditional frontend-heavy full-stack development.

• evin AI, Gemini CLI and/or similar tools. 
  

What You’ll Do 

You will bring expertise to contribute hands-on and to enable others to deliver high-quality solutions as part of a collaborative product engineering team. You will evaluate multiple approaches to solve problems and act as a velocity multiplier, actively mentoring and coaching other team members. By regularly reviewing and contributing to the refinement of your team's work, you’ll lend creativity to solving software problems using core development tools and techniques. You will lead a small team through the design, development and transition of the entire software stack, aligning data, API, and UI components to the norms of the rest of the software product. All while experimenting with new technologies and developing skills across the stack and contributing to the positive flow of your team.  

Specifically, you will: 

• Co-lead discovery and delivery project phases alongside other technologists 
  
• Collaborate with other engineers to iteratively design, develop, validate, and operate software at scale using agile principles 
  
• Lead or contribute to the development of implementation plans: designing the solution, setting patterns and practices and aligning to the product roadmap 
  
• Write high-quality application code using emerging technologies, frameworks, and GenAI tools, typically targeting cloud-based ecosystems 
  
• Provide oversight and guidance for other software engineers 
  
• Verify unit and integration level components using proven validation techniques 
  
• Develop supporting scripts and routines that automate procedures and help maintain components in production environments 
  
• Advance the craft by developing and sharing materials and thought leadership with others 
  

What You’ll Bring  

You leverage proficiency in developing full-stack solutions across a range of established and emerging technologies including: 

• Front end applications and modern web platforms. Specifically with experience in React, Vue and Typescript. 
  
• APIs and back-end services built using emerging frameworks and architectural patterns. Specifically with Python, NestJS and Rust. 
  
• Hosting, Generative AI, and application services provided by industry leading cloud providers. Specifically with AWS and GCP and familiarity with Terraform, AWS CDK, etc.  
  
• Experience using generative AI tooling in your own SDLC practice including Claude Code, GitHub Copilot, Cursor, Claude Code, OpenAI Codex, Kiro, Devin AI, Gemini CLI  and/or similar tools.