Limitless potential.

Company Background:

Genuine Parts Company founded in 1928 and based in Atlanta, Georgia, is a leading specialty distributor engaged in the distribution of automotive and industrial replacement parts and value-added services. The Company operates a global portfolio of businesses with more than 10,000 locations across the world, employing 60,000 people.

The GPC Global Technology Center in Krakow, established in 2022 by Genuine Parts Company is an innovative research and development facility supporting GPC’s digital transformation efforts.

The hub is focused on the development of advanced technologies and solutions that support GPC's operations and growth.

The GPC Global Technology Center team works on a wide range of projects assisting in areas such as e-commerce and data platforms, supply chain solutions, selling systems, and cyber security. This is home to a team of highly skilled IT engineers who are dedicated to driving innovation and delivering cutting-edge solutions for GPC.

Role purpose

As a Principal Engineer in the Generative AI Innovation Hub, you will set technical direction for enterprise generative AI capabilities while remaining hands-on in architecture and development. You will lead rapid experimentation, establish reusable engineering standards and reference architectures, and partner with product, data, platform, and security teams to move high-value use cases from prototype to secure, reliable production.

This senior individual-contributor role has broad influence across product and engineering teams. You will guide design decisions, mentor engineers, and communicate tradeoffs to technical and business leaders.

What success looks like

• Teams adopt reusable GenAI architectures, guardrails, and engineering standards.
• Experiments use clear business and technical criteria, leading to production or a timely stop decision.
• Production solutions meet agreed quality, security, privacy, reliability, latency, and cost targets.
• Mentoring and design reviews strengthen GenAI practices across engineering teams.

Key responsibilities

• Set and evolve the technical strategy, reference architectures, and engineering standards for enterprise generative AI solutions.
• Architect and build prototypes and production services using Python, APIs, cloud platforms, and modern software engineering practices.
• Lead the evaluation of foundation models, retrieval-augmented generation, agentic workflows, and model or vendor options, including build-versus-buy decisions.
• Establish LLMOps and MLOps practices for evaluation, testing, versioning, deployment, observability, incident response, and continuous improvement.
• Embed responsible AI, data protection, security, and human-oversight requirements throughout solution design and delivery.
• Partner with business and product leaders to frame use cases, define measurable outcomes, and prioritize experiments based on value, feasibility, and risk.
• Integrate generative AI capabilities with existing applications, enterprise data, and business processes.
• Lead architecture and design reviews, mentor engineers across teams, and raise the bar for engineering quality.
• Monitor relevant advances in AI and machine learning and recommend adoption when evidence supports meaningful business value.
• Promote disciplined experimentation with explicit learning goals, evaluation criteria, and decision gates.

Required qualifications

• 10+ years of software engineering experience, including significant technical leadership across complex enterprise or distributed systems.
• Demonstrated recent experience designing, delivering, and operating AI, machine learning, or generative AI solutions in production.
• Advanced Python skills and strong command of software architecture, APIs, testing, and maintainable engineering practices.
• Hands-on understanding of modern generative AI application patterns, including retrieval, agentic workflows, model selection, prompt design, and evaluation.
• Experience with cloud platforms, distributed systems, containers, CI/CD, and production observability.
• Ability to incorporate security, privacy, responsible AI, and operational risk requirements into technical designs.
• Proven ability to influence architecture and engineering decisions across multiple teams without relying on formal authority.
• Clear communication skills and the ability to explain complex technical tradeoffs to engineering, product, business, and executive audiences.

Preferred qualifications

• Bachelor's or master's degree in computer science, software engineering, or a related field, or equivalent practical experience.
• Experience with generative AI frameworks, vector search, model-serving platforms, and enterprise data integration.
• Experience modernizing large-scale enterprise platforms or building reusable internal technology capabilities.
• Experience evaluating AI vendors, commercial models, open models, and total cost of ownership.
• A record of mentoring senior engineers or building technical communities of practice.

Leadership characteristics

• Pragmatic curiosity and a bias toward evidence, learning, and measurable outcomes.
• Comfort navigating ambiguity, changing priorities, and rapidly evolving technology.
• A collaborative, inclusive leadership style that builds trust across disciplines and organizational boundaries.
• Strong judgment about when to experiment, when to standardize, and when to stop.

Location:

Krakow/Hybrid

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GPC conducts its business without regard to sex, race, creed, color, religion, marital status, national origin, citizenship status, age, pregnancy, sexual orientation, gender identity or expression, genetic information, disability, military status, status as a veteran, or any other protected characteristic. GPC's policy is to recruit, hire, train, promote, assign, transfer and terminate employees based on their own ability, achievement, experience and conduct and other legitimate business reasons.