SelectorAI SME Overview The Selector AI Engineer will serve as a technical subject-matter expert (SME) responsible for deploying, integrating, and supporting the Selector AI platform within the client's lab and infrastructure environments. This role is hands-on and highly technical, involving configuration, data integration, performance monitoring, documentation, and collaboration with client engineering teams to ensure the platform meets defined use cases and acceptance criteria. Key Responsibilities

• Assist the client with configuration, deployment, and integration of the Selector AI platform within the client's lab environment, providing hands-on, keyboard-level implementation support
• Collaborate with client engineers to develop, expand, and refine product use cases, including defining and validating acceptance criteria
• Support client engineers in monitoring the Kubernetes (K8s/MKS) cluster and underlying infrastructure that supports the Selector platform
• Identify, define, and help measure performance and operational KPIs
• Create and maintain technical documentation in collaboration with client engineers, including:
• Platform usage within the client environment
• Operational runbooks and internal support procedures
• Assist with the setup, configuration, and validation of data feeds into the Selector platform
• Participate in data validation activities to ensure the platform is functioning as expected and delivering accurate, reliable insights
• Act as a Selector AI subject-matter expert, providing expert-level recommendations to the client on:
• Platform setup and configuration
• Integration patterns
• Metrics, monitoring, and performance optimization
• Ongoing operational support and best practices

Required Skills & Experience

• Hands-on experience deploying and supporting Selector AI or similar AIOps / observability platforms
• Strong background in Kubernetes (K8s) and containerized infrastructure monitoring
• Experience with data ingestion pipelines, data validation, and system integrations
• Familiarity with performance metrics, KPIs, and observability tooling
• Ability to collaborate closely with client engineering teams in a lab or pre-production environment
• Strong documentation skills with the ability to translate technical processes into clear operational guides

Preferred Qualifications

• Experience supporting enterprise or financial-services environments
• Background in AIOps, monitoring, or observability platforms (e.g., Selector, Datadog, Dynatrace, New Relic, Prometheus)
• Experience working in proof-of-concept (PoC) or lab environments prior to production rollout