• Own and evolve core Data In Flight services (ingestion, user and event APIs, identity, system events) with a focus on reliability, performance, and cost. • Lead the Elasticsearch strategy for our multi-tenant live user and event data: index and mapping design, shard strategy, and safeguards across shared clusters. • Design and enforce platform contracts (APIs, data models, SLAs, and usage limits) that let product teams move quickly while keeping the data platform safe and scalable. • Work across other data and application teams on initiatives such as Elasticsearch upgrades, guardrails, resharding, and event retention. • Provide senior technical leadership during incidents and reviews, turning operational learnings into durable platform improvements. • Mentor engineers on distributed systems, data modeling, and pragmatic use of Elasticsearch.

• Act as the guardian of technical standards, ensuring quality, simplicity, and security in the platform’s evolution. • Define and prioritize the technical roadmap aligned with the company’s strategic objectives. • Lead architectural discussions and explore new solutions, ensuring they are scalable, sustainable, and secure. • Monitor quality, reliability, and performance metrics, proposing continuous improvements. • Mentor and support the team in engineering best practices, code reviews, pair programming, and knowledge sharing. • Collaborate with Engineering Leads, Product, and other areas to align technical feasibility with business priorities. • Ensure observability, stability, and continuous evolution of production systems. • Be a technical influence beyond the team by participating in guilds, tech talks, and organizational initiatives. • Act and collaborate in guilds focused on continuous improvement.

• Collaborating with Data Science and Product teams to design, build, and ship LLM-powered features across our media buying platform. • Designing and building agents using frameworks including LangChain and LangGraph to automate complex media planning workflows. • Developing MCP servers and APIs that connect internal and external agents to our media buying toolkit. • Building and tuning agent orchestration workflows, including evaluation and observability, to ensure agents reason, plan, and execute reliably. • Rapidly experimenting with emerging agentic approaches while guiding stable, scalable architectural decisions.

• Help train large-language models (LLMs) to write production-grade code • Compare & rank multiple code snippets • Repair & refactor AI-generated code • Inject feedback into the RLHF pipeline