Senior AI Platform Engineer

About ABC Fitness

ABC Fitness is the #1 software provider for fitness businesses of any size, all around the world. Supporting 30K clubs, 650K coaches, 40M members, and processing more than $12B in payments every year, ABC Fitness provides scalable, data-driven solutions to simplify club management and engage members and clients.

From personal trainers, boutique studios, and gyms, to international franchise health clubs, ABC Fitness will move your business forward through technology and industry insights. Innovation is at the center of everything we do, across ABC Glofox, ABC Ignite, ABC Trainerize, and ABC Evo. Let's make your fitness vision a seamless reality, together. Learn more at abcfitness.com

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.

Employment Type: Full-time employment in Poland through our Employer of Record (EOR) partner, under a local employment contract.

ABC Fitness is building an intelligent platform for the fitness industry, with AI embedded into the products, workflows, and decision points our customers rely on every day. The AI Platform team builds the foundational capabilities that enable engineering and product teams to safely and reliably develop, deploy, and operate AI-powered experiences.

This includes the LLM gateway, developer tooling, coding agents, inline guardrails, evaluation frameworks, retrieval capabilities, and the platform services that support production AI use cases. Our customers are the engineers building these systems and the product teams delivering AI features to users.

As an AI Platform Engineer, you will own platform deliverables end to end. You’ll design, build, launch, instrument, and operate capabilities against clearly defined SLOs. You’ll make adoption easy through clean interfaces, thoughtful migration paths, and documentation that enables teams to use the platform independently.

You’ll also help establish a clear architectural boundary: mechanism belongs in the request path, while policy sits outside it. The gateway executes routing and other policies but does not define them. This is a highly hands-on role for someone who combines strong platform engineering fundamentals with practical experience building and operating reliable AI systems.

This is not a model-training or research role. We value demonstrated evidence of ownership and impact over a specific number of years or a previous job title containing “AI.”

WHAT YOU'LL DO

• Own an AI platform deliverable from problem definition through production launch, adoption, and ongoing operation.

• Design, build, and operate platform capabilities such as LLM gateways, retrieval systems, agents, developer tooling, evaluation frameworks, and inline guardrails.

• Define success criteria and measurable outcomes before building, then use production data and baseline metrics to guide future improvements.

• Design systems with clear SLOs , strong observability, predictable failure handling, and reliable operational processes.

• Develop and maintain clean interfaces, migration paths, documentation, and paved paths that make platform adoption simple for engineering teams.

• Build retrieval and agent systems with a clear separation between retrieval quality and answer quality, supported by meaningful evaluation strategies.

• Design for secure and controlled AI execution, including least-privilege access, multi-tenant isolation, permissioning, auditability, and operational boundaries.

• Apply strong distributed systems principles, including consistency, idempotency, concurrency, resilience, rollout strategies, and failure-mode analysis.

• Use coding agents and other AI development tools effectively while validating their output through testing, review, and sound engineering judgment.

• Carry operational ownership for the systems you build, including participation in an on-call rotation, incident response, and post-incident improvement.

• Partner closely with Engineering, Product, Security, Infrastructure, SRE, and Developer Experience teams to ensure platform capabilities are practical, scalable, and widely adopted.

• Establish and communicate reusable technical patterns, standards, and reference implementations for building production-ready AI systems.

WHAT YOU'LL NEED:

• Demonstrated experience building or operating production retrieval systems, agent platforms, AI tooling, or other AI -enabled capabilities that real users depend on.

• Strong software engineering experience designing and operating distributed systems within an existing architecture.

• The ability to identify and clearly reason about consistency, idempotency, concurrency, failure modes, multi-tenant isolation, security, and rollout risks.

• Practical understanding of AI system quality, including retrieval evaluation, answer quality, release gates, agent reliability, and cost per request.

• Experience designing systems with clear success criteria, measurable baselines, observability, and operational targets such as SLOs .

• Strong understanding of secure AI execution patterns, including least-privilege access, permissioning, auditability, and policy enforcement.

• Experience taking a platform or product capability from problem definition to production and remaining accountable for its reliability, adoption, and continued improvement.

• Ability to work effectively with coding agents and AI development tools, while independently verifying generated code and recommendations.

• A strong production mindset, including experience with incident response, on-call operations, monitoring, debugging, and post-incident learning.

• Excellent communication and collaboration skills, with the ability to make complex technical concepts accessible to engineering and product stakeholders.

• A customer-focused mindset: you understand that a platform capability that teams do not adopt is not a successful outcome.

• A One Team orientation and a willingness to create reusable capabilities that enable other engineering teams to move faster and more safely.

AND IT'S GREAT TO HAVE

• Experience with LLM gateways, agent frameworks, retrieval-augmented generation, evaluation systems, or AI developer platforms.

• Experience with FinOps, cost optimization, or usage-based measurement for AI systems.

• Experience in application security, policy enforcement, identity, access management, or secure platform design.

• Experience with search quality, ranking, experimentation, or relevance evaluation.

• Experience with OpenTelemetry, distributed tracing, AI observability, or production telemetry.

• Experience building multi-tenant infrastructure or internal developer platforms.

• Experience with developer advocacy, technical enablement, SDKs, documentation, or helping teams adopt new engineering capabilities.

• Familiarity with routing systems, API gateways, service orchestration, workflow engines, or platform control planes.

What Success Looks Like

• You own at least one platform deliverable end to end, with clear success criteria, measurable baseline metrics, and a stated SLO .

• Engineering teams can adopt the capability through a clean interface, clear do