Gametime United - Senior AI & ML Engineer

• Production LLM Engineering: Proven experience shipping LLM-powered features to real users, including prompt engineering, tool use / function calling, structured outputs, and retrieval patterns. • Production LLM Engineering: • Evaluation & Testing: Hands-on experience building eval frameworks, prompt regression suites, LLM-as-judge pipelines, or similar quality infrastructure for AI systems. • Evaluation & Testing: • Python Proficiency: Deep fluency in Python as the primary development language, including familiarity with LLM SDKs and multi-agent frameworks such as OpenAI Agents SDK. • Python Proficiency: • Backend & Infrastructure: Solid backend engineering fundamentals including APIs, state management, data pipelines, and cloud infrastructure. • Backend & Infrastructure: • AI-First Engineering: You use AI agents daily to ship code. Experience with Claude Code, Codex, Cursor, or similar agentic coding tools is required. You direct agents, review their output, and help the team accelerate the development lifecycle. • AI-First Engineering • AI-Augmented Development Practices: Own and evolve our AI-augmented development practices. You’ll build the context files, guardrails, review processes, and test strategies that make agent-driven development safe and fast. • AI-Augmented Development Practices: • Agentic Development Leadership: You don’t just use AI tools - you teach others how. You’ve helped a team adopt agentic workflows, built prompt libraries, or established review processes for agent-generated code. • Agentic Development Leadership: • Collaborative Ownership: Contributes effectively to larger initiatives. Comfortable proposing solutions and iterating based on feedback from a tech lead. • Collaborative Ownership: • Clear Communication: Articulates technical decisions, tradeoffs, and progress concisely to both technical and non-technical stakeholders. • Clear Communication: • Mentorship: You can teach others. We shouldn’t need to teach you AI-first — you should be teaching us. • Problem-Solving and Decision-Making: • Pragmatic Builder: Balances speed with quality. Ships fast without leaving a trail of tech debt. Knows when to cut corners and when not to. • Pragmatic Builder: • High Agency: You move without permission. High agency, low drama. You take responsibility for outcomes in ambiguous situations. • High Agency: • Define the minimum educational and experiential requirements necessary to apply for the role. • Education: Bachelor’s degree in Computer Science, Engineering, or a related field. • Education: • Experience: 5–8 years of professional software engineering experience, with at least 1 year of building LLM-powered or AI/ML systems in production.] • Other Requirements: [Skills like language proficiency, technical tools] • Include any additional qualifications or experience that are not essential but would be beneficial. • Hands-on experience with multi-agent orchestration patterns (handoffs, agents-as-tools), tool-use frameworks, or complex agentic workflow coordination. • Prior experience with ML model serving infrastructure, feature stores, or ML data pipelines. • Performance Metrics: • Outline specific measures of success in the role. These should be aligned with the key competencies and job responsibilities. • AI Delivery: Directly contribute to the buildout of the AI build team’s weekly ship goals and leverage learnings to build out platform featuresAI First: Contribute to direct team-related code repos reaching Level 4 by building context files, implementing automated checks to act as agent guardrails, and reducing PR review times through agentic workflows.AI Advisor: Serve as a technical advisor to other teams that are building out agents and agentic workflows. Help teams implement best practices – everything from tracing LLMs and token management to evaluation datasets and building LLMs as a judge. • AI Delivery: Directly contribute to the buildout of the AI build team’s weekly ship goals and leverage learnings to build out platform features • AI First: Contribute to direct team-related code repos reaching Level 4 by building context files, implementing automated checks to act as agent guardrails, and reducing PR review times through agentic workflows. • AI Advisor: Serve as a technical advisor to other teams that are building out agents and agentic workflows. Help teams implement best practices – everything from tracing LLMs and token management to evaluation datasets and building LLMs as a judge. • At Gametime pay ranges are subject to change and assigned to a job based on specific market median of similar jobs according to 3rd party salary benchmark surveys. Individual pay within that range can vary for several reasons including skills/capabilities, experience, and available budget.