Artificial Intelligence Engineer

AI Platform Adoption Engineer

About the Role

This role exists to close the gap between AI platform capability and real-world usage in a

technical, developer-heavy organization. You will embed directly with engineering and technical

teams to identify where AI platforms are creating friction, where adoption is stalling, and why

and then fix it. This is not a training or documentation role. Your users are experienced software

developers and senior technical professionals who need a peer-level technical resource who

understands both their workflows and the AI platform landscape deeply enough to drive

meaningful change. You will work closely with the Team Lead and operate as the primary

interface between the platform operations team and the engineering organization.

Scope boundary: This role focuses on platform adoption, workflow integration, and friction

removal not AI skill development. Prompt engineering methodologies, RAG pipeline

design, vector search implementation, and AI application architecture are owned by the

Engineering team. This role does not duplicate or overlap with that charter.

Responsibilities

• Embed with engineering and technical teams to conduct structured workflow

assessments: identify where AI tools fit, where they are underused, and what operational

or integration barriers are preventing adoption.

• Diagnose and resolve adoption blockers at the platform integration level — IDE plugin

configuration, API authentication, network/proxy issues, enterprise SSO integration, and

tool compatibility — working alongside developers, not around them.

• Own the platform integration catalog: document which AI tools work with which internal

systems, pipelines, and developer environments, and keep it current as platforms

evolve.

• Monitor usage analytics by team and platform; identify low-adoption patterns, investigate

root causes, and drive targeted interventions in partnership with team leads.

• Evaluate new AI platform capabilities and integrations as they release; assess fit against

internal workflows and recommend adoption or deferral with technical justification.

• Manage the internal AI platform feedback loop: collect structured signal from developers

on capability gaps and platform pain points, synthesize findings, and route them to

vendors with technical specificity.

• Partner with the Team Lead on platform governance enforcement: ensure teams are

operating within approved configurations, data handling policies, and access controls.

• Collaborate with the Support Engineer to identify systemic platform issues vs. individual

user errors; escalate and coordinate resolution with vendors when warranted.

Required Qualifications

• 4+ years of hands-on software engineering, platform engineering, or developer tooling

experience; must be able to read and write code and hold a credible technical

conversation with senior engineers.

• Direct experience using enterprise AI platforms in a professional software development

context — not just familiarity, but evidence of integrating AI tools into real engineering

workflows.

• Strong diagnostic skills across the developer toolchain: IDE integrations, CLI tools, API

authentication, proxy and network configuration, CI/CD pipelines, and version control

systems.

• Proven ability to engage with tenured technical professionals as a peer; able to assess

workflows independently and make credible recommendations without being directed.

• Comfort with usage analytics and data: able to pull platform telemetry, build adoption

dashboards, and translate metrics into operational decisions.

Mandatory Skills

• Coding: Working proficiency in at least one of Python, JavaScript/TypeScript, Go, Java,

or Bash/PowerShell — sufficient to read unfamiliar code, write automation scripts, and

reproduce a developer's problem locally.

• Enterprise AI tooling: Hands-on work with at least one of Anthropic Claude (Code or

Cowork), GitHub Copilot, Cursor, Google Gemini for Workspace, or OpenAI ChatGPT

Enterprise in a deployment, administration, or integration capacity.

• Toolchain breadth: Demonstrated troubleshooting in at least three of: IDE extension

deployment (VS Code, JetBrains); CLI tooling; REST APIs with OAuth, API keys, or

bearer tokens; corporate proxies, TLS inspection, or network egress controls; CI/CD

(GitHub Actions, Jenkins, GitLab CI, Azure DevOps); Git-based source control (GitHub,

GitLab, Bitbucket).

• Identity: Practical understanding of enterprise SSO — SAML or OIDC — as it applies to

third-party developer tool integration.

• Data: SQL or an equivalent query capability, plus experience with a reporting or BI tool

such as Looker, Tableau, Power BI, or Grafana.

• Documentation: Experience authoring technical documentation in a wiki or docs

platform (Confluence, Notion, Markdown-based sites) for an engineering audience.

Preferred Qualifications

• Working knowledge across multiple enterprise AI platforms at a technical integration

depth — Anthropic Claude (Code and/or Cowork), Google Gemini for Workspace,

OpenAI ChatGPT Enterprise — sufficient to compare integration behaviors, troubleshoot

platform-specific issues, and advise on fit.

• Experience in a developer platform, internal tooling, or developer experience (DX)

function within a software product company.

• Familiarity with agentic AI deployment patterns (tool use, MCP servers, multi-step

workflows) at an operational level — understanding what can go wrong and how to

govern it, not how to build it.

• Background in enterprise developer tooling ecosystems: IDE plugin administration,

GitHub/GitLab integration, developer portal management.