Own DevOps and infrastructure for MLOps and agentic AI systems, establishing reusable patterns for CI/CD, scalable inference, orchestration, observability, and cost control. Design secure, scalable, repeatable systems using Infrastructure as Code (IaC) to support R&D workloads. Build secure CI/CD & automation systems: Enable secure tool access, workload isolation, and infrastructure for LLM-backed APIs and MCP servers, while partnering with security and compliance on access control, infrastructure governance and auditability. Ensure Reliability & Observability: Implement monitoring, logging, and alerting. Tune observability for ML-specific workloads to ensure performance, reliability, and operational insight. Provide Technical Leadership: Offer hands-on leadership across DevOps and platform initiatives. Review code, enforce best practices, improve tooling, and promote clean, well-tested infrastructure. Cross-Functional Collaboration: Partner with ML, software, and platform engineers to design deployment strategies, scope work, manage agile deliverables, and meet milestones. What You'll Bring Extensive DevOps Experience: 8–12+ years in DevOps, SRE, or platform engineering, including senior/lead roles. Experience designing end-to-end infrastructure systems, solving scale/performance challenges, and operating platforms in production. Cloud & Infrastructure Expertise: Strong skills in cloud platforms (AWS, Azure, or GCP) and AI/ML components such as Data
