*Client prefers someone onsite 3 days a week but could be flexible for remote if they exhaust local options-
Selector AI SME will be creating. proof of concept.
If they are a BigPand SME and has some decent experience with Selector AI would be considered.
Selector AI 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
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