Sr Software Engineer - Kubernetes

About this role

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Skills you'll use

What you'll do

• Design, build, and operate full-stack capabilities spanning backend services,frontend experiences and data pipelines.
• Build and scale backend services in Java and Python, designing clean, well-documented REST APIs with a focus on reliability and performance.
• Deploy, scale, and troubleshoot services on Kubernetes.
• Develop and operate distributed, high-throughput systems for ingestion, streaming, and storage, including Kafka-based streaming pipelines and ClickHouse-backed data stores.
• Develop performant, reusable frontend components using ReactJS or Angular with modern JavaScript/TypeScript.
• Design and integrate AI-powered features and agentic workflows into products — applying LLMs, retrieval, and agent frameworks to solve real customer problems.
• Partner with product management, design, and peer engineering teams to take features from concept to production.
• Own quality end to end — instrumentation, observability, testing, and performance at scale.

Who can apply

• Participate in design and code reviews and help raise the technical bar across the team.
• Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
• 6+ years of professional software engineering experience building and operating production systems.
• Backend proficiency in both Java and Python, with hands-on experience designing and building REST APIs.
• Hands-on experience with Kubernetes and containerization (Docker), including deploying and operating services in production.
• Desired frontend skills: ReactJS or Angular, JavaScript/TypeScript, and solid HTML/CSS fundamentals.
• Demonstrated experience designing distributed systems — scalability, fault tolerance, and messaging/streaming (e.g., Kafka).
• Hands-on experience or strong working knowledge of AI/ML — building with LLMs, AI agents, MCP or agentic frameworks, and applying them in production or product contexts.

About ServiceNow

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