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Principal AI Engineer
Overview 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 Work cross-functionally with Product/Design to translate ambiguous problems into measurable outcomes and shippable iterations. Lead the estimation of work at the quarterly level. Provide alternative solutions and negotiate with Product Management.
Build and shipLLM-powered product capabilitiesend-to-end: APIs, orchestration logic, integrations, and production rollouts
Design and implementagentic workflows(tool use, planners/routers, multi-step reasoning, handoffs, memory/state management) using modern orchestration frameworks (e.g., Lang Chain/Lang Graph-style)
Develop strongprompting + context engineeringpractices, including prompt versioning and experimentation (e.g., A/B testing approaches)
Buildevaluation harnessesfor LLM apps: curated test sets, automated checks, offline/online metrics, human review loops, and regression gates for changes
Collaborate with platform/infra teams to deploy onAWS, ensuring reliability, security, scalability, latency, and cost controls
Be an active participant in the day-to-day agile activities of the team including sprint planning, daily standups, sprint reviews, and retrospectives
Instrument systems forobservability(quality, latency, token/cost, errors) and create feedback loops to improve model+system behavior over time
Develop high quality solutions; leads the creation of new standards, patterns, and best practices
Write architecture & design documentation for new products, systems, and patterns
Review the work of teammates to ensure quality and adherence to system architecture and best practices; identify where new designs will require architectural changes
Mentorjunior developers to grow their skills
Troubleshoot and resolve complex and highly escalated customer/QA found issues in a timely manner
Experiment with new technologies that can enhance our tech stack.Provide new technology insights to remainder of development team.
Lead the implementation of development process improvements that add efficiency for the entire development team
Participate in build vs. buy analysis and complex decision making
Conduct regular, self-guided study to stay current on new and existing technologies
Participate in the hiring process and technical screens with an aim of attracting and hiring the very best engineers; work to expand the onboarding efficacy and vision
May represent the team through presentations to other company departments and customers
QUALIFICATIONS Engineering or Comp Science degree or equivalent work experience
9+ years of professional experience in software engineering, applied ML, data science engineering, or AI product engineering
Strong Python skills, experience building backend services (e.g. Flask/FastAPI) and integrating with REST APIs
Some applied ML / data science experience (e.g., experiments, model evaluation, error analysis, feature work, or shipping ML-informed product changes), even if your recent focus has shifted toward software/AI engineering
Practical understanding of LLMs/SLMs and how they behave in production (hallucinations, prompt sensitivity, latency/cost tradeoffs)
Hands-on experience building LLM applications (RAG, tool use, agents, workflow orchestration), with the ability to demonstrate end-to-end ownership from prototype‚ production‚ measurement, iteration
Experience with cloud infrastructure (e.g. AWS) and shipping production workloads (containers, deployments, monitoring)
Strong engineering fundamentals: testing, debugging, performance thinking, and clean, maintainable design
Excellent communication skills and ability to collaborate effectively in fast-paced, agile environments
Comfortable learning new technologies and system
OTHER QUALIFICATIONS Prior role experience as a Data Scientist / Applied ML Engineer, with evidence of taking prototypes closer to production (directly or in close partnership with engineering)<
