Principal AI Engineer (AI Enablement Platform)

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.



 RESPONSIBILITIES:

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.

QUALIFICATIONS:

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 p