Lead Software Engineer, ITC
NIKE, Inc. (company site, opens in a new tab)7+ yrsKarnatakaOn-siteDirect career pageEst. 35–55 LPA
Posted 15h ago
Reach people at NIKE, Inc.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at NIKE, Inc. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own the design, deployment, reliability, and monitoring of scalable Agentic AI platforms and enterprise services in production.
- Lead technical design for distributed systems and Kubernetes-based services; build reusable frameworks and tools, and optimize ML workflow performance and compute costs.
- Translate business needs into secure, reliable AI solutions with product, ML/MLOps, and domain technology teams; provide technical leadership and mentorship.
Minimum Requirements
- Work Experience Required: 7+ years in software development.
- Strong expertise in scalable, highly available distributed systems, cloud-native architectures, and AI/data platforms; object-oriented programming in Java, Python, or Go; and Kubernetes/Docker.
- Experience with databases (RDBMS/NoSQL), distributed caches, messaging pipelines such as Kafka/RabbitMQ, and REST or WebSocket APIs.
- Experience building and delivering production Agentic AI systems, including LLM orchestration, prompt engineering, RAG patterns, and agentic architectures—not only prototypes.
Ideal Candidate Profile
- Has delivered enterprise AI/LLM applications beyond prototypes and can address production concerns such as grounding, guardrails, governance, latency, cost, and observability.
- Combines distributed-systems engineering with hands-on ownership of deployment, uptime, security, automated testing, and performance optimization.
- May be especially competitive with practical experience in agent frameworks or protocols, vector stores/knowledge graphs, ML cloud platforms, and infrastructure-as-code tools; these are listed as additional or nice-to-have capabilities.