Role Description We’re looking for a Senior AI Platform Engineer to help design, build, and scale our AI and data infrastructure. In this role, you’ll focus on architecting and maintaining cloud-based MLOps pipelines to enable scalable, reliable, and production-grade AI/ML workflows, working closely with AI engineers, data engineers, and platform teams. Your expertise in building and operating modern cloud-native infrastructure will help enable world-class AI capabilities across the organization. If you are passionate about building robust AI infrastructure, enabling rapid experimentation, and supporting production-scale AI workloads, we’d love to talk to you. - Design, implement, and maintain cloud-native platform to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock. - Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics. - Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments. - Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production. - Drive best practices for observability, including monitoring, alerting, and logging for AI platforms. - Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types. - Stay current with new tools and technologies to recommend improvements to architecture and operations. - Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG). Qualifications - 7+ years of professional experience in software engineering and infrastructure engineering. - Extensive experience building and maintaining AI/ML infrastructure in production, including model, deployment, and lifecycle management. - Expert-level coding skills in TypeScript and Python building robust APIs and backend services. - Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows. - Expert level understanding of containerization (Docker), and hands on experience with CI/CD pipelines, orchestration tools (e.g., ECS) is a plus. - Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads. - Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK. - Ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members. - Strong collaboration skills and the ability to partner effectively with cross-functional teams. Nice to Have - Familiarity with emerging LLM frameworks for advanced prompt orchestration and programmatic LLM pipelines. - Understanding of LLM cost monitoring, latency optimization, and usage analytics in production environments. - Knowledge of vector databases / embeddings stores (e.g., OpenSearch) to support semantic search and RAG. Benefits - 100% employer-paid benefits for all eligible employees and immediate family members. - Unlimited paid time off (PTO). - 401K. - Flexible working arrangements - Remote work. - Company paid Life Insurance, LTD/STD. - A culture of continuous improvement where you can grow your career and get coaching.

• Design and build a self-service developer platform from the ground up — software catalog, golden-path templates, and developer portal experience using Backstage • Stand up and own GitOps-based continuous delivery with Argo CD or Flux CD, including app-of-apps patterns, sync policies, and progressive rollout • Build and maintain automated workflow orchestration using Argo Workflows — CI pipelines, scheduled jobs, and event-driven automation • Architect, operate, and scale production Kubernetes environments that the rest of the platform runs on • Design and run saturation and load testing with k6 — building reusable test templates and wiring them into the delivery pipeline • Implement policy enforcement and admission control with Kyverno, including signed/verified container image policies • Develop and use automation tools effectively to operate, manage, and scale production and development environments quickly • Design and execute new platform capabilities while continuously improving existing ones • Participate in regular customer and internal EverOps scrums • Provide operational support and project deployments for our customer environments

• Help establish engineering standards and operational practices for a growing ecosystem of internally developed applications and AI-enabled tools. • Support internal builders using Claude Code and related AI development environments by providing guidance around source control, modular design, testing, deployment, documentation, and maintainability. • Help implement production-grade operational practices across internal systems, including monitoring, alerting, incident response, deployment workflows, environment management, uptime management, and post-incident review processes. • Maintain and expand the company’s Azure Fabric Lakehouse architecture across bronze, silver, and gold layers. • Build and monitor ingestion pipelines, improve reliability and observability, resolve data quality issues, and help translate business questions into well-modeled reporting datasets and semantic layers. • Work directly with operational, billing, credentialing, and clinical leadership to support reporting requests, operational tooling, and scalable self-service data access.

• Design and operate AI platforms: Build and manage the enterprise AI platform, integrating multiple LLM providers with routing, fallback strategies and cost control. • Develop reusable AI services: Develop scalable APIs and services to support agents, copilots and business applications throughout the organisation. • Build intelligent agents and workflows: Design autonomous and multi-agent systems capable of reasoning, planning and executing complex business processes. • Enable enterprise integrations: Drive end-to-end automation by connecting AI solutions to enterprise platforms such as Microsoft 365, SharePoint, Salesforce and Jedox. • Implement RAG architectures: Develop advanced retrieval systems using hybrid search, vector databases and semantic indexing to ensure accurate, grounded outputs. • Optimize AI performance: Ensure high system reliability while monitoring and improving latency, cost efficiency and inference performance. • Establish evaluation frameworks: Define metrics and testing strategies to ensure the accuracy of the AI, detect hallucinations, and evaluate its overall quality. • Ensure AI safety and governance: Maintain compliance and trustworthiness by implementing guardrails, monitoring systems and governance frameworks. • Manage AI lifecycle: Oversee prompt versioning, model management and CI/CD pipelines to maintain robust production deployments.