Senior AI Platform Ops Engineer

As a Senior AI Platform Ops Engineer, you will join a multidisciplinary team responsible for building and operating the core AI platform capabilities that power internal and customer-facing AI solutions. This role extends traditional DevOps ownership into AI gateway services, model and agent deployment, MCP hosting, MLOps workflows, platform security, and production-grade observability.

Responsibilities:

• Design, build, and operate scalable cloud infrastructure and delivery pipelines for AI platform services across development, UAT, and production environments.
• Build and support AI platform CI/CD patterns for APIs, models, agents, MCP servers, and supporting services using container-based deployment standards and rollback automation.
• Own the runtime operations of AI gateway capabilities, including request routing, authentication, load balancing, retry policies, circuit breakers, and semantic caching.
• Build and maintain model deployment patterns, artefact management workflows, and inference pipelines for enterprise AI and ML use cases.
• Support agent and MCP platform operations, including standardised templates, health checks, autoscaling, monitoring, RBAC, and service-to-service authentication.
• Partner with data, AI, security, and application teams to enable vector databases, graph databases, feature stores, and RAG pipelines as reusable platform services.
• Implement platform guardrails such as PII detection, prompt injection protection, data leakage prevention, and use-case-specific business rules.
• Build deep observability for AI services, including latency, throughput, error rates, token usage, cost telemetry, audit logs, and operational dashboards.
• Drive production reliability through failover design, disaster recovery practices, incident support, runbooks, and continuous operational improvement.
• Support API platform integration patterns using Apigee X for secure exposure, routing, policy enforcement, onboarding, and monitoring of AI-related services.

Requirements:

• 7+ years of experience in DevOps, platform engineering, cloud infrastructure, or site reliability engineering, with strong production operations ownership.
• Strong hands-on experience with cloud platforms, microservices, CI/CD, containers, automation, and infrastructure design.
• Strong experience with Docker, Kubernetes, Linux administration, Git workflows, and system administration fundamentals.
• Hands-on development experience in Python and at least one additional language such as Node.js or Java.
• Experience with MLOps concepts such as model packaging, artefact management, deployment pipelines, inference operations, and rollback strategies.
• Experience operating AI or data platforms such as Databricks, MLflow, Airflow, or similar enterprise ML tooling.
• Strong understanding of observability, logging, alerting, and performance monitoring for distributed systems and AI workloads.
• Experience with secrets management, access control, and auditability in enterprise environments.
• Working knowledge of Apigee X or similar enterprise API gateway platforms.

Preferred qualifications:

• Experience with AI gateway architecture, model catalogues, agent platforms, or MCP hosting patterns.
• Experience with vector databases, graph databases, feature stores, and RAG pipeline operations.
• Experience with AKS or other managed Kubernetes services, managed identities, Key Vault, TLS, SSO, and enterprise platform runbooks.
• Experience working in Agile delivery models with strong cross-functional communication and a bias for action.