• Lead the design and implementation of scalable AI architectures that align with client business objectives and industry best practices • Collaborate with account leadership to identify and shape AI opportunities, contributing to business development efforts including RFP responses, white papers, and client presentations • Architect and build AI platforms and pipelines leveraging cloud-native services (AWS, Azure, GCP, Palantir, Databricks) to support model development, deployment, and monitoring • Apply deep knowledge of AI/ML lifecycle, including data preparation, model training, evaluation, and operationalization, with a focus on performance, scalability, and maintainability • Drive hands-on development of AI solutions, including Generative AI and Agentic AI systems, ensuring robust integration with enterprise systems and data sources • Collaborate with cross-functional teams (data engineering, software development, DevOps) to ensure seamless delivery of AI solutions • Provide technical leadership and mentorship to junior AI engineers and data scientists • Ensure AI solutions comply with commercial and public sector standards for responsible AI, data governance, and security.

• Design and deliver production-grade agentic AI systems on Azure AI Foundry, ensuring solutions go well beyond PoC and into scalable, enterprise-ready platforms. • Architect end-to-end pipelines leveraging Azure OpenAI, vector search, tool-calling patterns, and LangChain / Semantic Kernel for orchestration. • Define and enforce REST API design standards, JSON schema definitions, and event/message-based integration patterns across AI workloads. • Lead the evolution of solutions from MVP to productized platforms, including refactoring, hardening, and meeting non-functional requirements around performance, reliability, observability, and security. • Deploy and oversee scalable Azure infrastructure (AKS, App Service, Functions, or equivalent), collaborating with DevOps/SRE teams to define runbooks, SLIs/SLOs, and monitoring frameworks. • Evaluate and integrate emerging agent frameworks and patterns (MCP, A2A) to continuously modernize platform capabilities. • Use LangSmith or equivalent tooling to implement tracing, evaluation, and quality assurance across agentic pipelines. • Document architectures, sequence diagrams, and interaction flows using Visio, Figma, or similar tools; facilitate collaborative design sessions using Mural/Miro with distributed teams. • Partner with cross-functional and multi-program teams to ensure platform components are reused and versioned effectively across products. • Support incident management and post-mortems, contributing to a culture of continuous improvement in live production contexts.

• Architect, implement, and refine cutting-edge multiplayer-ready gameplay systems in and outside of the Unreal Engine • Produce elegant, efficient, and maintainable C++ code • Leverage expert-level understanding of data structures and algorithms to solve interesting challenges • Collaborate with cross-functional teams to deliver innovative gameplay experiences • Contribute to the development of scalable and robust networking solutions • Tackle and resolve intricate technical hurdles in complex multiplayer games

• Build, iterate, and maintain AI agents within the architecture and boundaries defined by the Lead AI Engineer • Own your agents end-to-end: prompt design, tool wiring, context routing, failure handling, and output validation • Integrate data components across media platforms — ingesting, normalizing to schema, and routing to the correct agent context • Work within defined data contracts and surface schema drift before it becomes a runtime failure • No feature enters development without defined success criteria and regression tests • Run prompt benchmarking, track output quality across model versions, and flag hallucination patterns or quality regressions proactively • Build and maintain ETL/ELT pipelines supporting daily automated callouts and weekly optimisation reporting • Operate within and extend the MCP connector library for external platform APIs • Build and maintain Slack-based approval flows — agent callouts, feedback capture, exception alerts, and operational notifications • Own the reliability of your agents in production • Monitor output quality, respond to incidents, drive root-cause fixes rather than surface patches