Platform Engineer (AI Infrastructure)

About the job Overview The GxP Digital Solutions division at ATP CGPharm builds AI-driven products that support regulated decision-making and operational workflows for major pharmaceutical and biotech clients. We are looking for a hands-on Platform Engineer to own cloud infrastructure, deployment pipelines, and developer experience for AI-driven products in regulated pharma environments. The focus is DevOps/platform engineering: building reliable, secure, and cost-effective platforms on AWS and Azure and taking solutions from development to production. This is a broad role for someone who enjoys working across infrastructure, CI/CD, data pipelines, integrations, and application delivery. You will work closely with AI engineers, consultants, and client stakeholders. When needed, you should also be able to support AI engineering tasks such as LLM API integration, RAG workflows, evaluation pipelines, and model deployment - without losing the core focus on platform, reliability, and operations. Responsibilities Cloud Infrastructure & DevOps Design and manage cloud infrastructure on AWS and Azure, including compute, networking, identity, storage, serverless services, and container platforms. Implement Infrastructure as Code using Terraform, Pulumi, CloudFormation, Bicep, or similar tools. Build and maintain CI/CD pipelines for Python backend, Next.js frontend, and AI/data workloads. Deploy and operate containerized applications in production; Kubernetes experience such as EKS/AKS is preferred. Create streamlined local development environments, reusable templates, and internal tooling. AI Product Delivery, Data & Integrations Support data pipelines for AI systems, including training data, RAG workflows, analytics, and operational data feeds. Integrate with client systems and enterprise platforms through APIs, data feeds, and secure connectivity patterns. Support AI engineering tasks when needed, including LLM API integration, evaluation workflows, model deployment, and application debugging. Ensure data quality, traceability, and lineage for regulated environments. Reliability, Security & Compliance Set up monitoring, logging, alerting, and operational dashboards for production systems. Define and track SLOs/SLIs, manage incidents, and conduct post-mortems. Implement security best practices: secrets management, access control, vulnerability scanning, and secure deployment workflows. Maintain system documentation, validation evidence, and audit-relevant records for GxP compliance. Optimize cloud and LLM API costs through practical FinOps practices. Core Skills Strong experience with AWS and/or Azure across compute, networking, identity, storage, and serverless services. Infrastructure as Code experience with Terraform, Pulumi, CloudFormation, Bicep, or similar tools. CI/CD tooling experience with GitHub Actions, GitLab CI, Azure DevOps, or similar. Container technologies such as Docker, Podman, container registries, and ideally Kubernetes in production. Monitoring and observability experience with Datadog, Grafana, Prometheus, CloudWatch, Azure Monitor, or similar. Scripting and automation skills using Python, Bash, PowerShell, or similar. Ability to read, debug, and contribute to Python and/or Node.js applications when needed. Good understanding of APIs, data flows, and integration patterns in enterprise environments. Security-first mindset and experience working in compliance-oriented environments. Clear communicator who can work with AI engineers, consultants, client IT teams, and non-technical stakeholders. Nice to Have Experience in pharma, life sciences, or other regulated industries, including GxP and GAMP5 environments. MLOps experience: model deployment, model registries, versioning, evaluation, and monitoring. Experience with RAG systems, LLM APIs, or hosting custom/fine-tuned models using vLLM, Text Generation Inference, SageMaker, Azure AI, or similar platforms. Data engineering experience, including ETL pipelines, orchestration, and data quality frameworks. Experience integrating with enterprise systems such as SAP, LIMS, MES, document management systems, or data historians. Multi-tenant SaaS architecture or secure client-environment deployment experience. Profile Hands-on platform engineer with a DevOps mindset and enough AI engineering awareness to support the team when needed. Comfortable moving between infrastructure work, application debugging, pipeline improvements, data/integration topics, and client-specific requirements. Able to own technical topics end-to-end across multiple client projects. Pragmatic, adaptable, and proactive - someone who automates repetitive work and improves team productivity. Cares about reliability, security, compliance, developer experience, and practical delivery. What We Offer Join a newly established digital solutions practice within a trusted 30-year GxP consultancy. Direct exposure to top-tier pharma and biotech clients. High-impact projects where your work supports regulated manufacturing operations and digital decision-making. Broad role with variety across infrastructure, applications, data, AI, and integrations. Small team, high autonomy - opportunity to shape the platform, delivery approach, and team culture. Competitive compensation and flexibility. Preferred Startdate: 15 July 2026