• Build and Scale the AI Developer Tools Team: Hire, onboard, and develop a high-performing engineering team from the ground up with expertise in AI/LLM integration, platform engineering, and developer experience; establish team culture, technical standards, and operating norms • Ship AI-Powered Developer Tools: Deliver production-ready AI agents for developer productivity (automated PR reviews, code generation, documentation), observability insights (anomaly detection, root cause analysis), and operational automation (deployment pre-reviews and failure investigations, incident response assistance) • Build Self-Service AI Developer Tools Platform: Create and operate the foundational infrastructure that enables teams across Docker to build, deploy, host, and scale their own AI developer tools including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, cost management, and operational tooling • Drive Platform Adoption and Developer Experience: Establish the AI Developer Tools platform as the default path for teams building AI-powered tooling; deliver self-service capabilities, comprehensive documentation, templates, and best practices that reduce time-to-production from weeks to days • Partner on AI Strategy and Technology: Work closely with engineering leadership and Agent Dev technical leadership to align on AI technology choices, architectural patterns, and integration strategies; stay current on LLM advancements and developer tooling trends • Explore Productization Opportunities: As internal AI developer tools demonstrate value, partner with product management and go-to-market teams to evaluate commercial viability; prototype, validate, and transition successful internal tools into customer-facing product offerings • Deliver Measurable Impact: Define and track success metrics for both AI developer tools (adoption rates, productivity gains, time saved) and platform capabilities (time-to-deploy new tools, number of teams using platform, operational efficiency) • Cross-Functional Collaboration: Partner with product engineering teams (Hub, Registry, Cloud/AI, Scout, Accounts & Billing), platform teams (Infrastructure, Security, Data), and product leadership to understand requirements, gather feedback, and align on priorities • Operational Excellence: Ensure reliability, security, and performance of AI developer tools and the hosting platform; establish SLOs, monitoring, and incident response for production AI systems serving internal developers • Team Development and Culture: Mentor and grow engineers on your team; foster a culture of experimentation, rapid prototyping, and learning; attract diverse talent excited about AI and developer experience

• 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