• Design and implement AI solutions using Azure AI Foundry, Azure OpenAI, Copilot Studio, and Microsoft Fabric • Build reusable AI services, agent frameworks, prompt registries, evaluation pipelines, MCP integrations, and tool catalogs • Establish engineering standards for enterprise AI development • Create reference architectures for agents, RAG (if required), tool calling, multi-agent workflows, and Human-In-The-Loop (HITL) workflows • Provide technical guidance on when to use Copilot, Foundry, Agentforce, ChatGPT Enterprise, Claude Enterprise, and private models • Design AI-enabled solutions leveraging OneLake, Lakehouse, Data Warehouse, Data Factory, and Power BI • Establish reusable AI-ready data patterns and provide guidance to data engineering as required • Provide guidance on monitoring, telemetry, evaluation frameworks, token tracking, cost allocation, prompt performance, and agent reliability metrics • Support deployment, rollback, versioning, and incident response • Implement prompt versioning using GitHub, agent approval workflows, model routing controls, tool access controls, and usage monitoring. Automate quality control using agents in collaboration with innovation architect • Being part of the Emerging Technology team, collaborate with other team members as well as IT and business functions • Translate business use cases into reusable technical solutions

• 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