Senior Data, AI Engineer (Only W2)

This individual should be comfortable working across backend services, data pipelines, APIs, and AI-enabled knowledge capabilities, with a strong instinct for proactively identifying failures and data-quality issues. Implement production-readiness practices including automated testing, CI/CD, containerization, model versioning, monitoring, observability, drift detection, performance optimization, troubleshooting, and continuous improvement.

Must Have Technical/Functional Skills

• Senior backend engineering experience within enterprise environments
• Strong data pipeline and data integrity experience
• Experience moving, transforming, and synchronizing content, articles, and

• structured/unstructured data between systems

• AI/LLM integration experience, including RAG, MCP, agents, and/or AI-enabled knowledge tools
• Strong API, integration, and automation experience
• Observability-first engineering mindset, including logging, metrics, alerts, dashboards, and health checks
• Experience with Splunk and Datadog
• Strong SQL skills and experience troubleshooting data-related issues
• Development experience with Java/Spring Boot, Python, Node.js, TypeScript, or similar technologies
• Cloud engineering experience, particularly AWS
• CI/CD pipelines and production deployment experience
• Experience with incident management, troubleshooting, and root cause analysis
• Ability to build self-service tooling and automation that reduces manual support and dependency on other teams
• Strong documentation, communication, and cross-team collaboration skills
• Experience supporting Knowledge Management, training, developer enablement,

• or content platforms is a strong plus

Responsibility of / Expectations from the Role

• Senior engineer responsible for improving the reliability, integrity, and observability of data and content moving across
• the Knowledge Management ecosystem. This individual should be comfortable working across backend services,
• data pipelines, APIs, and AI-enabled knowledge capabilities,
• with a strong instinct for proactively identifying failures and data-quality issues.

• Implement production-readiness practices including automated testing, CI/CD, containerization,
• model versioning, monitoring, observability, drift detection, performance optimization,
• troubleshooting, and continuous improvement.
• Generic Managerial Skills, If any

Very good communication and stakeholders management in multi vendor team interactions