Staff AI Platform Developer
New
J
JobgetherAI Platform Engineering
Remote position based in the United States.Full-TimeStaff
Salary167,000 - 230,000 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- DockerPythonGitKubernetesTypeScriptCI/CDRESTful APIsPrompt EngineeringLLM
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.
Responsibilities
- 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.
- Establish and document reusable AI architecture patterns, engineering standards, and best practices to enable consistent development across teams.
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