AI Engineer – DTC Platform Engineering

SPEED & SPIRIT is what we look for in our candidates defined by some simple values that inspire us to BE DRIVEN in our performance BE VIBRANT in our sporting legacy BE TOGETHER in our team spirit and to let our individual talent and experience shine. Applying for a job at PUMA is easy.

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About the Organisation & Role:

As part of theDTC Platform Engineering organisation you will help deliver the shared platforms engineering services and foundational infrastructure that enable product teams to build test deploy and operate software securely and reliably at scale. Joining ourAI-Native Engineering Enablementpillar as anAI Engineer you will focus on production-grade solutions to build deploy and scale LLM-powered applications and agentic this role you will bridge the gap between core software engineering modern machine learning and tangible business impactmoving generative AI capabilities out of experimental prototypes and into resilient high-performance production systems for our primary customers: the product engineering teams.

WHAT YOU WILL DO:

Implementing Agentic Workflows

• Construct multi-step AI agents and orchestration workflows using frameworks like LangChain LlamaIndex or LangGraph.

• Well-versed with Agentic design patterns.

• Develop production features using foundation models via cloud AI infrastructure

• Deploy and configure enterprise AI and agentic gateways to handle model routing semantic caching load balancing rate limiting and cost/token tracking.

• Designs and secures the universal integration layer that connects AI models to enterprise databases internal codebases and APIs using the Model Context Protocol.

Implement RAG & Retrieval Pipelines

• Build semantic search document ingestion and Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding modelsgraph databases etc

• Implementation of Vectorless approach wherever required.

Implement AI Memory Architectures:

• Design and maintain advanced cognitive memory systems including episodic memory semantic memory and conversational state management across sessions using graph and in-memory databases.

Production & MLOps:

• Deploy version and monitor AI services using containerization and cloud infrastructure.

• Configuring the right serving architecture based on business needs such as real-time APIs (REST/gRPC) batch prediction jobs or edge deployment.

• Administering central hubs (like MLflow or Weights & Biases) to track model versions lineage code and artifacts.

• Managing cloud infrastructure costs by auto-scaling compute resources and selecting the right hardware (CPUs vs. GPUs) for production workloads.

• Securing model endpoints managing access controls (IAM) and ensuring data privacy compliance

Develop AI Test & Evaluation Harnesses:

• Build Automated Evaluation (Auto-Eval) platforms and continuous integration (CI/CD) testing harnesses to stress-test prompts RAG pipeline outputs and multi-agent systems at scale.

Cross-Functional Collaboration:

• Partner closely with product managers engineering managerSREs Infosec team and frontend/backend software engineers to deliver end-to-end user experiences.

• Bachelors or Masters degree in Computer Science Software Engineering Artificial Intelligence or a related field.

• 4 years of software development experience with a dedicated focus on building testing and shipping agentic workflows memory systems and LLM features to production for 2 years

• Proven track record of deploying stateful AI systems complex tool-calling agents or enterprise API gateway integrations.

• Hands-on experience with multi-agent design patterns state machines (LangGraph) and stateful memory management tools.

• Practical knowledge of configuring AI gateways for routing fallbacks and cost tracking.

• Experience building evaluation harnesses using frameworks like DeepEval Ragas etc.

• High proficiency inPython (and/or TypeScript for node-based agent runtimes). Rust /Java understanding is good to have.

• Experience with vector stores (Pinecone pgvector Milvus Qdrant) and embedding models.

• RESTful/GraphQL APIs Docker Kubernetes and enterprise cloud infrastructure (AWS GCP or Azure).

PUMA provides equal opportunities for all job applicants regardless of race color religion national origin sex

gender identity or expression sexual orientation age or disability. Equality for all is one of the core principles at

PUMA and we do not tolerate any form of harassment or discrimination.

At PUMA every application is reviewed by real people who are committed to fairness transparency and equal

opportunity - no matter your background identity or ensure our process stays true to these values no automated systems or AI tools are used to make hiring decisions. Every decision is made by real people -with real judgment and accountability. We may use functions supported by Artificial Intelligence (AI) to carry out isolated organizational steps such as scheduling interviews. These functions have no influence on decisions in the application process. We believe in creating spaces where everyone is welcome celebrated and empowered to contribute authentically. Because at PUMA whoever wants to play can play.

PUMA is a global sports brand creating footwear apparel and accessories that inspire athletes and everyday movers. The PUMA Group owns PUMA Cobra Golf and stichd operates in 120 countries and has around 22000

employees worldwide.