Staff AI Engineer

Our client is a rapidly scaling global leader in Sports Tech, combining high-growth SaaS platforms with integrated payments processing ($13B+ processed annually). They have expanded from $150M to $400M in revenue with double-digit international growth across 20+ countries, maintaining a high-impact, wellness-focused culture. They are building a shared AI Enablement Platform that allows every product team across the company to ship AI-powered features quickly, safely, and at scale.

• Significant hands-on engineering experience (10+ years) with strong backend/platform engineering expertise.
• Recent experience building and operating production backend/platform systems with deep involvement in code, APIs, infrastructure, observability, debugging, and tradeoffs.
• Proven experience building production-grade AI/LLM systems such as RAG, agent workflows, tool-calling systems, AI APIs, or LLM-powered product capabilities.
• Strong programming experience, preferably in Python and backend service stacks for production APIs and distributed systems.
• Deep understanding of API design, service boundaries, SDKs, integration patterns, reliability, testing, observability, performance, and cost optimization.
• Practical experience with LLM application architecture: context engineering, retrieval patterns, tool use, structured outputs, orchestration, fallback handling, and evaluation.
• Ability to build evaluation and validation systems for AI applications, including golden datasets, scenario tests, regression checks, and quality gates.
• Experience deploying and operating cloud-based production systems on AWS, GCP, Azure, or similar.
• Strong technical judgment to make tradeoffs between speed, reliability, safety, cost, developer experience, and business impact.
• Ability to lead through technical contribution: coding, reference implementations, design reviews, mentoring, and turning ambiguous needs into working systems.
• Proven ability to influence across teams via architecture reviews, design documents, technical standards, mentorship, and collaboration.

Nice to have:

• Experience building internal developer platforms, SDKs, shared services, or paved-path tooling.
• Hands-on experience with LLM observability/evaluation tools like Langfuse, LangSmith, or OpenTelemetry tracing.
• Familiarity with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or custom orchestration frameworks.
• Experience with vector databases, embedding workflows, semantic search, retrieval tuning, and RAG productionization.
• Experience using AI-native engineering tools like Cursor, GitHub Copilot, Claude, ChatGPT, or similar.
• Experience writing technical blogs, internal guides, architecture documents, or enablement materials.
• Experience leading technical standards or architecture forums across multiple teams.

Build the AI platform foundations:

• Write and ship production code for core AI platform services, APIs, orchestration components, evaluation systems, SDKs, and internal developer tools.
• Design, build, and operate reusable AI platform capabilities such as chat APIs, RAG services, summarization, classification, semantic search, prompt/workflow execution, and agent orchestration.
• Build high-quality APIs, SDKs, templates, and reference implementations for product teams.
• Create production-ready orchestration patterns including retrieval, tool use, validation, fallback handling, memory/state management, and human-in-the-loop workflows.
• Review PRs, debug production issues, improve reliability, and make technical tradeoffs.
• Partner with SRE, Security, Platform, and Product Engineering teams for reliability, scalability, security, observability, and cost-awareness.

Own evaluation, reliability, and production readiness:

• Build evaluation harnesses including test sets, scenario evaluations, regression checks, quality gates, and release criteria.
• Implement AI observability practices such as tracing, prompt/version tracking, output quality monitoring, latency tracking, token/cost visibility, and feedback loops.
• Design safe defaults including structured outputs, tool permissions, prompt-injection awareness, data handling controls, fallback paths, and failure modes.
• Ensure production fundamentals: testing, monitoring, rollout strategy, incident readiness, performance tuning, cost management.
• Help teams move from prototypes to production by addressing gaps in reliability, observability, security, evaluation, and readiness.

Drive AI adoption:

• Establish internal standards via patterns, standards, documentation, examples, architecture reviews, and enablement.
• Work with product teams to unblock challenges and produce production-ready features.
• Lead technical design reviews and architecture forums for AI systems.
• Identify repeated friction points and convert into reusable platform capabilities.
• Raise AI engineering capability with demos, enablement sessions, office hours, technical write-ups, and blogs.

Lead through technical influence:

• Act as a hands-on technical leader and multiplier.
• Set technical direction and align stakeholders across Product, Engineering, Security, SRE, and Data.
• Mentor engineers on production AI engineering, backend design, system reliability, evaluation, and workflows.
• Help teams use AI-assisted engineering tools responsibly to improve development without compromising quality.

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