Lead Software Engineer, ITC

Nike's AI Platform engineering team builds scalable, secure, and intelligent AI platforms and services, with a focus on Generative AI, to power decision-making and personalised experiences across the Nike ecosystem. The role involves designing and building robust AI platforms and services with scale, performance, and cost optimisation in mind. The engineer will work in a fast-paced, collaborative environment, deliver scalable and reliable software platforms and services, and provide technical leadership across cross-functional teams.

• Design, build, deploy, and own complete, scalable, and reliable Agentic AI and software platforms and services.
• Lead the technical design of distributed systems and Agentic AI services, including DevOps using Kubernetes.
• Build reusable frameworks, libraries, and data management tools to improve productivity.
• Optimise the performance and cost-efficiency of ML workflows and compute layers.
• Collaborate with ML/MLOps engineers, business domain technology teams, and product managers to translate business requirements into technical solutions and ensure experiences are optimised for usability and performance.
• Lead the development and production of agentic and AI/LLM-powered enterprise systems.
• Implement security best practices to protect data and ensure compliance with industry standards.
• Develop and maintain automated tests to ensure the quality and reliability of the software.
• Provide technical leadership and mentorship to junior engineers.
• Ensure the uptime, reliability, and monitoring of the systems built and owned.

• 7+ years of experience in software development, with a strong foundation in object-oriented development, distributed systems, cloud-native architectures, and AI/Data platforms.
• Strong expertise in building scalable and high-availability distributed systems
• Object-oriented programming languages such as Java, Python, or Go
• Container technologies such as Kubernetes or Docker
• RDBMS/NoSQL databases
• Distributed caches
• Messaging pipelines such as Kafka or RabbitMQ
• And REST or WebSocket-based APIs.
• Experience building Agentic AI systems, LLM orchestration, prompt engineering, RAG patterns, and agentic architectures, including having built and delivered AI-powered solutions rather than only prototypes.
• Understanding of LLM production concerns, including grounding, guardrails, AI governance, latency, cost optimisation, and observability.
• Familiarity with knowledge graphs, vector stores, relational databases, and embedding pipelines.
• Advanced knowledge of one or more ML cloud platforms (AWS SageMaker, Databricks, Azure ML, or GCP) and its ecosystem of services.
• Proficiency with Infrastructure as Code (IaC) tools such as Terraform or AWS CloudFormation.
• Strong understanding of advanced data modelling techniques and modern data warehouses.
• Ability to design scalable, fault-tolerant, and maintainable distributed systems.

• Experience building agents using modern frameworks and protocols (MCP, A2A, LangGraph, Crew AI, or similar), with meaningful delivery beyond tutorials, whether in production, a significant internal project, or a customer-facing PoC.