Job Summary

• Own the end-to-end technical design and delivery of Decision Hub capabilities (eligibility, arbitration, offering services, monitoring, APIs, etc.).
• Champion and lead the adoption of AI-assisted development practices (e.g., GitHub Copilot, generative AI for code/test generation) to accelerate delivery, improve code quality, and foster a culture of innovation.

Key Responsibilities

• Own the end-to-end technical design and delivery of Decision Hub capabilities (eligibility, arbitration, offering services, monitoring, APIs, etc.).
• Champion and lead the adoption of AI-assisted development practices (e.g., GitHub Copilot, generative AI for code/test generation) to accelerate delivery, improve code quality, and foster a culture of innovation.
• Spearhead the integration of Generative AI, adaptive models, and other emerging AI technologies into the platform's core capabilities, moving beyond traditional ML models to create a truly intelligent system.
• Collaborate with Product, Solution Architecture, Data, and Security to ensure compliance with regulatory controls, data lineage, and auditability.
• Drive performance, scalability, and resilience design; validate via capacity/performance testing and production dry runs.
• Translate business requirements into extensible, maintainable technical solutions and reference designs for multi-market reuse.
• Lead technical design and code reviews, setting the standard for high-quality, efficient, and maintainable code.
• Hands-on implementation and troubleshooting; unblock teams during critical incidents and deployment windows.
• Coach and grow engineering capability; set engineering practices, quality metrics, and a high-performance culture.

Skill Requirements

• 8+ years in software engineering with 3+ years in technical lead or architect roles on distributed, real-time systems.
• Proven experience designing and delivering decisioning, orchestration, or real-time personalization platforms (or equivalent large-scale event/streaming systems).
• Strong cloud design and development experience (GCP preferred; AWS/Azure acceptable) and familiarity with multi-cloud/on-prem tradeoffs.
• Hands-on experience with the end-to-end machine learning lifecycle (MLOps), from model integration and deployment to performance monitoring and feedback loops.
• A strong passion for and practical experience with leveraging AI development tools (e.g., GitHub Copilot, CodeWhisperer) and embedding them into team workflows.
• Deep knowledge of performance engineering, capacity planning, fault tolerance, and observability (APM, metrics, tracing, alerting).
• Hands-on with modern engineering practices: microservices, APIs, CI/CD, infra as code, automated testing, security controls.
• Excellent stakeholder skills: ability to translate complex technical concepts for business partners and influence product and delivery decisions.

Strong mentoring and team leadership track record.

Other Requirements