SelectorAI SME

Job Title: SelectorAI SME

Job Location: NYC Hybrid with Client preferring 3 days onsite per week but this could be flexible

Duration: 3 Month

Responsibilities

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