• Design and own the data foundation behind Phoenix’s agentic commerce platform. • Turn raw commerce events into structured, decision-grade intelligence that powers autonomous payment routing, approval probability scoring, checkout optimization agents, and merchant copilots. • Architect the event models, pipelines, and data contracts that allow AI agents to observe, reason, act, and improve in real time. • Design for low latency, high correctness, replayability, and observability. • Define how intelligence is embedded into core commerce behavior and how Phoenix’s systems learn from every transaction to continuously improve merchant performance.

• Configure and maintain platform environments across TMS platforms (e.g., XTM, Phrase, memoQ, Trados), including MT engine connections, workflows, AIPE/AIQE parameters, and provider-level settings (glossaries, dictionaries, model selection). • Act as the primary diagnostics layer between production teams and AI/ML Engineering. Investigate MT quality issues, identify root causes (data, engine, pipeline, or TMS), and escalate with clear reproduction steps and context. • Manage end-to-end MT operations: data preparation, TM analysis, engine training, evaluation, deployment, and monitoring. Maintain relationships with MT providers and identify opportunities to optimise performance. • Support pre-sales as an MT/AI subject matter expert. Build ROI models, prepare QBR insights (e.g., edit distance trends, MT savings, cost usage), and advise clients on MT strategy, including engine selection and content suitability. • Identify and eliminate manual processes through automation. Design or build tools, scripts, and workflows that reduce human intervention and improve scalability. This is a core expectation of the role.

• Design and build the core infrastructure for the Agent Platform • Own the platform APIs, orchestration layers, and runtime systems for AI agents • Develop the marketplace for deploying autonomous intelligence • Collaborate with cross-functional teams to enhance the platform

• Architect agents that don't just answer questions but do work • Give agents secure, governed access to the systems and data they need to act • Design, build, and deploy MCP servers across two tracks • Build the OAuth and credential management layer • Work with R&D Architecture to define production-ready standards for Placer MCP server • Triage incoming connector requests and bug reports from across the company • Build and maintain Cowork plugins and Claude skills • Take ownership of Placer's internal BI environment on Databricks • Build and maintain tooling to track internal AI tools usage • Optimize compute, storage, and networking across the internal AI platform • Implement security best practices for AI platforms