• Design and implement graph data models and entity networks using Neo4j. • Develop and optimize Cypher queries for relationship and network analysis. • Build and maintain vector databases using Postgres (pgvector), Milvus, or Weaviate. • Implement embedding ingestion pipelines for similarity and semantic search use cases. • Design and manage data repositories / lakehouse layers using Apache Iceberg. • Develop data ingestion and transformation pipelines from multiple source system. • Deploy and operate databases and data platforms on Nutanix infrastructure. • Ensure performance tuning, scalability, availability, and fault tolerance. • Implement data quality checks, monitoring, and error handling. • Collaborate with AI/ML, analytics, and application teams. • Document data models, architectures, and operational procedures.

• 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