Senior Software Engineer, Content AI

We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere.

The Content AI team in Warsaw applies AI to content-related processes, including search, voice assistant capabilities, subtitles, live captions, translations, metadata tagging, and content generation workflows.

• 6+ years of software development experience building high-performance, scalable backend cloud application systems.
• BS or MS degree in Computer Science, Engineering, Mathematics, or a related technical field.
• Strong expertise in Kotlin, Python, or similar backend programming languages.
• Strong experience with backend frameworks such as Spring Boot, Bottle, Gunicorn, or similar JVM/Python frameworks.
• Experience designing and building APIs using REST, gRPC, or GraphQL.
• Experience with relational and NoSQL databases such as PostgreSQL, MySQL, DynamoDB, or MongoDB.
• Hands-on experience with AWS, Docker, Kubernetes, CI/CD, and infrastructure tooling such as Terraform, GitHub Actions, or Jenkins.
• Strong understanding of distributed systems, Elasticsearch optimization, caching, observability, security, performance optimization, production troubleshooting, and incident response.

Nice to have:

• Experience working on AI-powered products, model-serving systems, or backend services that integrate with ML models.
• Experience with NLP, ASR, voice assistants, chatbots, search platforms, live captions, subtitles, translations, or content metadata workflows.

• Design, develop, and operate scalable backend APIs and microservices powering search, voice, subtitles, captions, translations, and content workflows.
• Lead technical discussions, architecture reviews, design decisions, and task breakdowns within the engineering team.
• Build services with a focus on high availability, low latency, security, scalability, and maintainability.
• Work with ML Engineers to integrate hosted NLP, ASR, and AI models into production backend systems.
• Architect reusable code, libraries, patterns, and standards that can be adopted across teams.
• Use monitoring, tracing, profiling, and load-testing tools to identify bottlenecks and improve production performance.
• Champion engineering best practices, including CI/CD, test automation, feature toggles, logging, monitoring, and reliable release processes.
• Mentor engineers through code reviews, pair programming, technical guidance, and incident response leadership.