Staff AI Platform Developer

Job Description

• This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff AI Platform Developer based in the United States. This is a senior, hands-on engineering role focused on building the internal AI platform and tooling that enables teams to work smarter and more efficiently. You’ll architect and develop practical solutions using LLMs, AI agents, retrieval-augmented generation, workflow automation, and AI-assisted development. The role spans the full journey from early prototypes to secure, reliable, production-ready services integrated with critical business and engineering systems. You’ll partner closely with software, product development, and DevOps teams to identify high-value opportunities and turn ambiguous business needs into scalable technical solutions. A key focus will be establishing reusable AI platform capabilities, responsible-AI guardrails, observability, permissions, evaluation, and cost controls. You’ll also help drive adoption by sharing best practices, coaching engineering teams, and ensuring solutions deliver measurable business value. This is an opportunity to shape how a technically sophisticated organization adopts AI while working on challenging problems in a highly collaborative environment.
• Accountabilities: Lead the architecture and implementation of internal AI solutions, including LLM applications, AI agents, retrieval-augmented generation (RAG) systems, workflow automation, and AI-assisted development tools. Drive the adoption of practical AI capabilities across Product Development teams by identifying high-value opportunities that improve productivity, quality, security, or employee experience. Build prototypes and production-ready services that integrate with internal systems such as GitHub, Jira, Slack, documentation platforms, service desks, and business applications. Partner with DevOps teams to design reusable platform components covering model access, prompt and configuration management, permissions-aware retrieval, logging, monitoring, evaluation, and AI cost tracking. Evaluate and select AI models, APIs, tools, and vendors based on quality, reliability, security, privacy, latency, maintainability, and cost. Design and implement responsible-AI guardrails covering sensitive-data handling, access controls, prompt-injection protection, output validation, audit logging, and human-in-the-loop review. Transition production-ready AI solutions to Product Development teams for long-term ownership, maintenance, and support. Help teams automate internal workflows and adopt AI capabilities in practical, secure, measurable, and sustainable ways. Establish and document reusable AI architecture patterns, engineering standards, and best practices to enable consistent development across teams. Monitor adoption, user feedback, operational reliability, performance, and costs to ensure AI solutions deliver measurable value. Promote responsible AI practices, including usage policies, security controls, data-handling standards, and appropriate governance. Lead AI office hours and facilitate technical knowledge sharing, mentoring, and collaboration across engineering teams. Participate in annual hackathon initiatives and help transform promising technical ideas into working solutions. Join the on-call rotation approximately 6–12 months after starting to support the availability and reliability of production applications. Requirements: Bachelor’s degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent professional experience. 7+ years of professional experience in software engineering, platform engineering, internal tooling, DevOps, ML systems, or a closely related discipline. Extensive experience across the LLM application stack, including prompt engineering, RAG, embeddings, vector search, reranking, context management, structured outputs, and tool use. Strong experience with agentic orchestration frameworks and interoperability protocols such as MCP and ACP. Proven experience evaluating, securing, fine-tuning, and optimizing LLM systems for production environments, including cost and performance optimization. Strong proficiency in Python, TypeScript/JavaScript, or another modern programming language used for backend services and automation. Experience designing, implementing, and consuming REST APIs and backend services. Hands-on experience with Git, CI/CD, Linux/Unix environments, containers, and production deployment workflows. Experience building AI solutions that securely leverage sensitive internal data and organizational knowledge sources. Strong understanding of security, privacy, access control, monitoring, reliability, and governance considerations for enterprise AI systems. Excellent written and verbal technical communication skills, including requirements gathering, architecture communication, and technical documentation. Ability to work effectively across teams, navigate ambiguity, understand business workflows, and translate them into practical technical solutions. Self-motivated, proactive, adaptable, curious, and committed to continuous learning. Experience with AWS, Kubernetes, Docker, Terraform, or comparable cloud and infrastructure technologies is a plus. Experience integrating AI solutions with Slack, Jira, GitHub, Confluence, Google Workspace, Salesforce, Zendesk, ServiceNow, or similar platforms is advantageous. Experience with self-hosted, open-source, or private AI model deployments is a plus. Background in quantum computing, scientific computing, hardware/software systems, or another highly technical domain is advantageous. Benefits: Remote position based in the United States. Base salary range of $167,000–$230,000 USD per year. Competitive total rewards package, with details on base compensation, equity, bonus, benefits, and perks reviewed during the interview process. Opportunity to contribute to meaningful, cutting-edge AI initiatives with broad organizational impact. Collaborative environment that values diverse perspectives, open communication, accountability, and continuous learning. Opportunities to grow technical leadership capabilities through cross-team collaboration, mentoring, and knowledge sharing. Participation in annual hackathon initiatives and opportunities to explore and develop new ideas. Opportunity to work on technically challenging problems at the intersection of AI, software engineering, and next-generation computing.
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