AI Platform / MLOps Engineer

Role purpose

Platform foundations and deployment

• Design and maintain development, testing, and production environments for training, batch scoring, online inference, and LLM-enabled applications.
• Build CI/CD pipelines with versioned artifacts, automated checks, environment promotion, approvals, and tested rollback for code, models, prompts, and configuration.
• Implement model registries, experiment tracking, and reproducible environments linking deployed artifacts to code, data, and evaluation records.
• Operate model-serving endpoints, approved model gateways, and container workloads with controlled access to third-party and internally hosted models.

Observability and reliability

• Monitor latency, throughput, availability, error rates, resource utilization, and model or token costs across AI services.
• Instrument data-drift and model-quality signals with model owners, distinguishing infrastructure alerts from analytical performance alerts.
• Implement autoscaling, quotas, rate limits, timeouts, fallback behavior, and graceful degradation for service needs.
• Define service objectives, runbooks, and recovery procedures; test backups, restoration, and deployment rollback and participate in the incident-response rota.

Security and operational efficiency

• Apply infrastructure as code, least-privilege access, secrets management, network controls, and approved data-residency settings with Security and Infrastructure teams.
• Protect training data, model artifacts, and logs with access, encryption, and retention controls; prevent secrets or unnecessary client data from entering telemetry.
• Optimize CPU/GPU capacity, inference configurations, and cloud spend; make application and model costs visible to technical and business owners.
• Review provider changes, dependency vulnerabilities, and platform lifecycle risks; maintain documentation and reusable deployment patterns.

Essential experience and qualifications

• 5+ years in DevOps, SRE, platform engineering, or ML infrastructure, including deployment and operation of AI or ML workloads.
• Strong experience with a major cloud platform, containers, infrastructure as code, and CI/CD.
• Python or comparable scripting skills and practical Linux, networking, identity, and secrets-management knowledge.
• Experience with model serving, experiment tracking, artifact registries, and reproducible deployment practices.
• Experience building observability, capacity planning, incident response, and recovery processes for production services.
• Understanding of LLM APIs and model inference, including variable latency, usage limits, and cost controls.
• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent infrastructure engineering experience.

Preferred experience

• Kubernetes, MLflow, model-serving frameworks, and GPU workload optimization are preferred where relevant to the chosen architecture.
• Financial services infrastructure, 24/5 trading environments, and support for continuously available client systems are preferred experience.
• Private AI deployments, model gateways, and multi-provider resilience are preferred experience.