AI Engineer

• Enablement & coaching — work directly with engineers through 1:1s, pair programming, workshops, office hours, and cohorts to raise their effectiveness with AI-assisted development: prompting, workflow design, and model/tool selection. Identify under-adoption and lift teams up to the standard.
• Best-practice propagation — capture what works (and what fails) in one team and systematically transfer it: living best-practice guides, prompt libraries, troubleshooting playbooks, and an internal AI knowledge base and skills registry.
• Scale through champions — stand up and support a network of embedded AI champions so enablement scales past a single person.
• Unblocking & feedback loop — be the go-to when engineers get stuck with AI tooling. Proactively tour teams to surface blockers early, aggregate them into patterns, and escalate systemic issues to leadership.
• Internal integrations & tooling — build internal AI integrations and reusable tooling hands-on (Node.js/TypeScript, MCP) — from prototype to production independently, including auth, access control, security review, and CI/CD.
• Evaluation & scouting — run structured evaluations of AI tools, models, and internal integrations; monitor the fast-moving landscape (Claude Code, Cursor, Copilot/Codex, new model releases) and deliver curated, actionable recommendations.
• Measurement & impact — define and track adoption and productivity metrics by team, tool, and individual. Build dashboards on real usage and output — adoption/active-usage rate, assistant acceptance rate, PR throughput and cycle time — with a quality guardrail (change failure rate) so speed never comes at the cost of quality. Reference frameworks: DX Core 4, DORA.
• Cross-functional partnering — partner with security/compliance on safe-by-design guardrails, and with GTM to translate field needs into internal enablement and feed adoption patterns back.

Senior AI Enablement Engineer

• Build and operate LLM-powered internal solutions, tools, and automations end-to-end
• Ship customer-facing AI features
• Drive technical decisions from design through production rollout
• Own systems in production, including monitoring, reliability improvements, and incident response participation
• Build and maintain LLM evaluation workflows, including quality/safety checks, baselines, and regression detection
• Partner with R&D, product, and security teams
• Evaluate emerging AI technologies and patterns, and advocate adoption when justified

Applied AI Engineer

• Focuses on the hands-on building of AI-powered marketing tools, multimodal media processing (audio/video), and data analytics pipelines.
• Tool & Feature Engineering: Build and maintain custom LangChain tools that allow AI agents to interact with the web, CRMs, and internal SQL databases.
• Multimodal Processing: Implement PyTorch and OpenCV pipelines for analyzing and generating brand-consistent images, video, and audio.
• Data Analytics & Pipelines: Execute complex SQL queries and Pandas scripts to transform raw marketing data into insights for AI-driven decision tools.
• Endpoint Maintenance: Develop and document FastAPI endpoints for use by the marketing team's internal applications.
• Model Monitoring: Regularly evaluate model performance and fine-tune open-source transformers to improve accuracy for specific marketing use-cases.

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Senior AI Enablement Engineer

Applied AI Engineer

AI Engineer