Staff AI Engineer (AI Enablement Platform)
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AI Summary
Join ABC Fitness, the leading technology provider for the fitness industry!
Join ABC Fitness and become part of a culture that’s as ambitious as it is authentic. Let’s transform the future of fitness—together!
Our Values
Best Life
We believe great work begins with great people. That’s why our culture is built on respect, trust, and belonging. We create an inclusive environment where every team member can bring their authentic self to work—because diverse perspectives drive innovation and meaningful impact.
Growth Mindset
We are doers, thinkers, and dreamers. At ABC Fitness, your growth is our investment. Through continuous learning, mentorship, and professional development opportunities, we empower you to reach new heights—personally and professionally.
One Team
From day one, you’ll be part of a team that collaborates, celebrates, and cares. We move fast, support one another, and have fun along the way. Because when you thrive, we all thrive.
Build the platform that powers AI across ABC Fitness.
ABOUT THE TEAM
ABC Fitness is building a shared AI Enablement Platform that allows every product team to ship AI-powered features quickly, safely, and at scale.
The goal is simple: remove the plumbing so product teams can focus on customer value instead of reinventing AI infrastructure. This platform provides the paved path for building production AI systems across ABC, including:
Reusable APIs for chat, summarization, RAG, classification, and AI workflow execution
Agent orchestration patterns for multi-step reasoning, tool use, validation, and fallback handling
Evaluation and validation systems to measure output quality and prevent regressions
Observability, policy enforcement, cost controls, and production guardrails
Developer experience, documentation, templates, and reference implementations that help teams adopt AI faster
At ABC, we love entrepreneurs because we are entrepreneurs. We roll our sleeves up, we act fast, and we learn together.
ABOUT THE ROLE
As a Staff AI Engineer – AI Enablement Platform, you will be one of the core technical builders of ABC’s AI platform.
This is a deeply hands-on individual contributor role. You will be expected to write production code, design and build reusable platform services, review technical designs, debug production issues, and partner directly with product teams to drive adoption.
You will also influence technical direction, establish engineering patterns, and help teams adopt the platform, but your credibility will come from building real systems, not just advising from the sidelines.
This role is not focused on ML research, data science, or training models from scratch. We are looking for a strong platform/backend engineer with deep hands-on experience building production-grade LLM systems, APIs, orchestration layers, evaluation systems, and developer-facing tools.
You will help turn AI from isolated experiments into a repeatable engineering capability across ABC.
WHAT YOU’LL DO
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 that product teams can adopt with minimal friction.
Create production-ready orchestration patterns for retrieval, tool use, validation, fallback handling, memory/state management, and human-in-the-loop workflows.
Stay close to implementation details by reviewing PRs, debugging production issues, improving reliability, and making pragmatic technical tradeoffs with the team.
Partner with SRE, Security, Platform, and Product Engineering teams to ensure the platform is reliable, scalable, secure, observable, and cost-aware.
Own evaluation, reliability, and production readiness
Build evaluation harnesses for LLM-powered systems, including curated test sets, scenario-based 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 production feedback loops.
Design safe defaults for AI systems, including structured outputs, tool permissions, prompt-injection awareness, data handling controls, fallback paths, and failure-mode handling.
Ensure AI capabilities are built with strong production fundamentals: testing, monitoring, rollout strategy, incident readiness, performance tuning, and cost management.
Help teams move from prototypes to production by identifying gaps in reliability, observability, security, evaluation, and operational readiness.
Drive AI adoption across ABC
Establish the “ABC Way” of building production AI systems through patterns, standards, documentation, code examples, architecture reviews, and technical enablement.
Work directly with product teams to help them adopt the platform, unblock implementation challenges, and turn early AI ideas into production-ready features.
Lead technical design reviews and architecture forums for AI systems, ensuring teams make sound decisions around quality, safety, reliability, and maintainability.
Identify repeated friction across teams and convert it into reusable platform capabilities.
Help raise AI engineering capability across the company through internal demos, enablement sessions, office hours, technical write-ups, and engineering blog posts.
Lead through technical influence
Act as a hands-on technical leader and multiplier, not just an advisor.
Set technical direction for complex AI platform areas and align stakeholders across Product, Engineering, Security, SRE, and Data.
Mentor engineers on production AI engineering practices, backend design, system reliability, evaluation strategy, and AI-native development workflows.
Help teams use AI-assisted engineering tools responsibly to improve development speed, testing, debugging, documentation, and iteration without compromising quality.
WHAT THIS IS NOT
Not a data science role focused on analysis, experimentation, or dashboards.
Not an ML research role focused on training foundation models from scratch.
Not a prompt-only role without ownership of production systems.
Not building one-off AI features for a single product line.
Not an architecture-only role where you primarily create diagrams, review designs, or delegate implementation to others.
Not a people-management role. You will influence, mentor, and lead through technical depth, but this is an individual contributor position.
This is a hands-on platform engineering role focused on building the systems, standards, and paved paths that make AI production-ready across ABC.
WHAT YOU’LL NEED
Significant hands-on engineering experience, typically 10+ years, with strong backend/platform engineering depth.
Recent hands-on experience building and operating production backend/platform systems. You should be comfortable going deep into code, APIs, infrastructure, observability, debugging, and production tradeoffs.
Proven experience building and shipping 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/or backend service stacks used 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-based tests, regression checks, and quality gates.
Experience deploying and operating cloud-based production systems on AWS, GCP, Azure, or similar platforms.
Strong technical judgment and ability to make tradeoffs across speed, reliability, safety, cost, developer experience, and business impact.
Ability to lead through hands-on technical contribution: writing code, creating reference implementations, reviewing designs, mentoring engineers, and turning ambiguous platform needs into working systems.
Proven ability to influence across teams through architecture reviews, design documents, technical standards, mentorship, and hands-on partnership.
AND IT’S GREAT TO HAVE
Experience building internal developer platforms, SDKs, shared services, or paved-path tooling.
Hands-on experience with LLM observability/evaluation tooling such as Langfuse, LangSmith, Open Telemetry-based tracing, or similar tools.
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 with AI-native engineering workflows using tools like Cursor, GitHub Copilot, Claude, ChatGPT, or similar tools.
Experience writing technical blogs, internal engineering guides, architecture documents, or enablement material that helps engineering teams adopt new practices.
Experience leading technical standards or architecture forums across multiple engineering teams.
WHAT SUCCESS LOOKS LIKE
Adoption
Product teams across ABC use the AI Enablement Platform as the default starting point for AI-powered features.
Velocity
Teams can move from AI idea to production feature significantly faster because reusable APIs, SDKs, templates, evals, and guardrails are already available.
Trust
AI systems are observable, measurable, debuggable, and safe to operate in production.
Quality
Teams have clear evaluation practices, regression checks, and release gates for LLM-powered workflows.
Reuse
Repeated AI patterns become shared capabilities instead of one-off implementations across product teams.
Influence
The “ABC Way” of building AI systems becomes widely understood through architecture reviews, documentation, internal demos, and engineering best practices.
AI-native engineering
Engineering teams use AI tools and platform capabilities to improve how software is designed, built, tested, reviewed, documented, and operated.
WHY THIS MATTERS
If we get this right, AI at ABC Fitness will not be a collection of isolated experiments. It will become a core engineering capability that every team can use to build better products faster.
This role will help define how ABC builds AI-powered software: production-first, safe by default, measurable, reusable, and deeply integrated into the way engineering teams work.
WHAT’S IN IT FOR YOU:
Purpose led company with a Values focused culture – Best Life, One Team, Growth Mindset
Time Off – with our open PTO policy, you pick when you want time off when it’s best for you!
Days of Disconnect – once a quarter, we take a collective breather and enjoy a day off together around the globe. #oneteam
Parental & Pawternity Leave – we offer leave for when your family grows by two feet or four paws!
Fitness Perk – we are committed to meeting you wherever you are in your fitness journey with a quarterly reimbursement.
Discounts – access to discounts with our partners, such as Dell, Microsoft & many more.
Medical/Dental/Vision coverage.
EAP – we get you help when you need it. Period.
Calm App +4 – enjoy tranquility with a Calm App subscription for you and up to 4 dependents over the age of 16.
And more! – so many benefits we couldn’t even fit them all here!
#LIREMOTE
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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