Staff AI Platform Developer [590]

D-Wave

11h•$167,000 - $230,000•Remote

About The Position

D-Wave is seeking a Staff AI Platform Developer to lead the architecture and development of internal AI tooling across the company. In this role, you will help us build practical tools using LLMs, AI agents, retrieval-augmented generation, workflow automation, and AI-assisted development capabilities. Your work will help D-Wavers work faster and more effectively while keeping our internal tools secure, maintainable, reliable, and cost-effective. As part of D-Wave's software organization, you will work with our Product Development teams to create AI-powered solutions to address high-value internal use cases that move the needle in terms of productivity, quality, security, or job satisfaction. You will be a hands-on architect and builder who can take ideas from prototype to production, determine when AI is the right solution, and select the AI-powered tools and technologies best suited to each use case. No quantum computing knowledge is required, but you will have the opportunity to learn about this next generation of computing.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent experience
7+ years of professional experience in software engineering, platform engineering, internal tool development, DevOps, ML systems, or a related discipline
Extensive experience developing across the LLM application stack: prompt engineering, retrieval-augmented generation (RAG), embeddings, vector search, reranking), context management, structured outputs, and tool use
Extensive experience with agentic orchestration frameworks and interoperability protocols (MCP, ACP)
Experience evaluating, fine-tuning, securing (guardrails), and cost-optimizing LLM systems in production
Strong proficiency in Python, TypeScript/JavaScript, or another modern language used for backend services and automation
Experience designing, implementing, and consuming REST APIs and backend services
Experience with Git, CI/CD, Linux/Unix environments, containers, and production deployment workflows
Experience building AI solutions that securely leverage sensitive internal data and company knowledge sources
Excellent written and verbal technical communication skills, including requirements capture and technical documentation
Ability to work across teams, clarify ambiguous problems, and turn business workflows into practical technical solutions
Self-motivated, proactive, flexible, curious, and committed to continuous learning

Nice To Haves

Experience with AWS, Kubernetes, Docker, Terraform, or other cloud and infrastructure tools
Experience integrating with Slack, Jira, GitHub, Confluence, Google Workspace, Salesforce, Zendesk, ServiceNow, or similar systems
Experience with self-hosted, open-source, or private AI model deployments
Experience in quantum computing, scientific computing, hardware/software systems, or other highly technical domains

Responsibilities

Drive the adoption of AI capabilities across D-Wave’s Product Development teams
Lead the architecture and implementation of internal AI solutions, including LLM applications, agents, RAG systems, and workflow automation
Build prototypes and production services that integrate with internal systems such as GitHub, Jira, Slack, documentation platforms, service desks, and business applications
Partner with DevOps to design and create reusable platform components for model access, prompt/configuration management, permissions-aware retrieval, logging, monitoring, evaluation, and cost tracking
Evaluate and select AI models, APIs, tools, and vendors with a focus on quality, reliability, security, privacy, latency, and cost
Design guardrails for data handling, access control, prompt injection, output validation, audit logging, and human-in-the-loop review
Transition production ready AI solutions back to the Product Development teams for long term ownership and ongoing support
Help teams automate internal workflows and adopt AI solutions in practical, safe, and measurable ways
Document patterns and best practices so other developers can build AI-enabled tools consistently
Track adoption, user feedback, operational reliability, and cost to ensure the AI solutions deliver measurable value
Promote responsible AI practices (usage policies, guardrails, and data handling standards) across development teams
Lead AI office hours and facilitate technical knowledge sharing across engineering teams.
Participate in the yearly D-Wave Hackathon where you can bring new ideas to life
Join the on-call rotation to help ensure the high availability and reliability of D-Wave applications – 6 to 12 months after starting