• Design and maintain scalable CI/CD systems, self-service workflows, and platform capabilities that support our engineering organization. • Lead and develop a team of 2-4 Platform Engineers through 1:1s, mentoring, goal-setting, and performance management. • Own technical architecture decisions for platform systems by designing solutions that balance developer experience, reliability, and operational simplicity. • Partner with engineering leadership and cross-functional teams to drive platform adoption and embed security, reliability, and compliance into workflows. • Drive hiring efforts by refining candidate profiles, interview rubrics, and ensuring assessments validate real-world platform engineering skills. • Foster a culture of ownership and continuous improvement where engineers are empowered to propose and drive solutions. • Ensure operational health of platform systems through incident response practices, SLAs, and systematic technical debt reduction. • Champion standardization by establishing golden paths for infrastructure-as-code, containerization, and observability.

• Design, build, and maintain scalable, secure, and resilient platforms. • Treat cloud infrastructure as a product and internal developers as customers. • Build self-service tooling, automated guardrails, and platform experience for engineers. • Automate infrastructure, optimise CI/CD pipelines, and ensure platform reliability and performance. • Drive Platform Engineering best practices, improve developer productivity, and foster operational excellence through infrastructure-as-code and modern tooling. • Kubernetes & Containerisation: Kubernetes, Istio, ArgoCD, Helm & CI/CD for containerised workloads. • Monitoring and Logging: Prometheus, Loki, Grafana & ELK to monitor resources. • Source Control & CI/CD: GitHub & GitHub Actions for version control and CI/CD. • Operating Systems: Ansible and AWS tooling to manage Unix environments. • Backend Development: Hands-on experience designing, developing, and deploying backend services in Java; scripting for ad-hoc problems. • Cloud Infrastructure: AWS/Cloud services and best practices. • Infrastructure as Code: AWS CDK & Terraform to manage infrastructure. • Architecture and Design Patterns: DDD, EDA, distributed computing architecture. • Computer Networking: Core networking concepts including VPCs, Subnets, NAT Gateways, etc. • Database Management: Relational databases (MySQL, SQLite, PostgreSQL), event pipelining using Kafka. • Internal Developer Platform: Build and maintain tooling that reduces cognitive load for product teams.

• Implement AI-enabled backend services within a secure, cloud-native microservices environment. • Design and scale Intelligent Document Processing (IDP) pipelines to extract and validate data from claims, authorizations, and medical documentation. • Integrate LLMs and intelligent agents into clinical and claims workflows to streamline patient and provider interactions. • Build AI Agents that surface actionable insights from EHR, claims, and unstructured clinical data. • Support cross-functional architecture sessions, code reviews, and design workshops. • Engineer modern AI/ML development practices, scalable system design, and secure operations. • Drive continuous improvement by identifying opportunities to enhance system performance, scalability, and maintainability. • Create design documentation and technical blueprints that guide implementation.

• Develop and enhance the machine learning platform to manage the full model life cycle • Build frameworks and tools to enable the data science team developing and enhancing predictive model support scalable real-time predictions in production • Design and implement data engineering solutions for model training • Expand NLP capabilities with advanced analysis techniques to improve text understanding • Design and implement high-performance, scalable services and applications • Collaborate with team members to create integrated solutions and ensure timely delivery of quality software and documentation • Understand and adhere to development standards for consistency across teams • Perform in-depth technical and performance analyses to troubleshoot production issues • Monitor and maintain production systems for reliability and efficiency