Lead Engineer - Ad Tech

You have8+ years of software development experience, with experience designing,buildingandoperatingcomplex distributed systems through at least one complete implementation lifecycle.

You have strong backend engineering fundamentals and are comfortable designing scalable APIs, microservices, asynchronous systems and data-intensive applications.

You are fluent inJava / Spring and microservices architecture, with experience buildinghighly availableproduction systems.

You have experience working with databases includingRDBMS and NoSQL technologies such as Cassandra andMongoDB, andunderstand datamodelingand storage trade-offs.

You have experience building distributed event-driven architectures using technologies such asKafka.

You understand Ad Tech business fundamentals and how technology supports businessobjectives, andcan translate business vision into technical strategy while understanding architectural and financial trade-offs.

Experience building or integratingDSP, SSP or Ad Server technology platformsin support of self-service advertising is preferred.

Applied AI & LLM Engineering

You have hands-on experiencedesigning,buildingandoperatingproduction applications using LLMs, beyond chat interfaces, promptexperimentationand proof-of-concept applications.

You have designed and optimized production-gradeRAG systems, with strong understanding ofdata ingestion and chunking, retrieval and ranking, context and grounding, hybrid retrieval, and evaluation.

You have experience withknowledge graphs and graph-based retrieval, and understand when to use vector search, Graph RAG, structured queries, traditionalsearchor hybrid approaches based on the problem and data.

You understand the engineering trade-offs of production LLM systems, includingquality, latency, cost, reliability, observability,securityand failure handling, and can systematically improve retrieval and overall system performance rather than relying primarily on prompt engineering.

Agentic Systems

You have designed or builtagentic applications or orchestration frameworkswhere LLM-powered components interact with tools, APIs, retrievalsystemsand other agents toaccomplishmulti-step tasks.

You understand concepts such as:

• Tool/function calling

• Agent planning and execution

• Workflow and state management

• Multi-agent orchestration

• Agent memory and context management

• Routing and delegation

• Human-in-the-loop workflows

• Guardrails and policy enforcement

• Retry, timeout and fallback strategies

• Agent observability and traceability

• Evaluation of agentbehaviorand task completion

You understand the distinction betweendeterministic and non-deterministic execution pathsand can design systems that deliberately combine both.

You know when a business workflow should remain deterministic and testable using conventional software and when probabilistic reasoning using an LLM or agent isappropriate.

You design AI systems assuming that model outputs can be incorrect or unpredictable and therefore incorporateappropriatevalidation, constraints, structured outputs, fallbacks, idempotency, observability and human interventionwhererequired.

AI Quality & Evaluation

You understand that production AI systems require rigorousquality and evaluation practicesbeyond model selection and prompting.

You have experienceestablishingoffline and online evaluation strategiesfor LLM-powered systems, covering retrieval and response quality,factual grounding, task completion, reliability,latencyand cost.

You can buildevaluation harnesses and regression testswith representative datasets,metricsand quality thresholds, and integrate them intoCI/CD as deployment and quality gates.

You can use evaluation,observabilityand tracing to diagnose quality issues acrossdata, retrieval, context, prompts,modelsand orchestration, and drive systematic improvements.

Engineering Leadership

You can translate ambiguous business problems into clear technical architectures and incrementally deliver solutions from experimentation through production.

You make architecture decisions based on measurable trade-offs rather than technology trends and are comfortable challenging unnecessary complexity.

You can leadPOCs and technical experimentswhile clearly distinguishing between whatdemonstratesfeasibility and what isrequiredtooperatethe capability reliably at enterprise scale.

You have proven technical leadership capabilities and the ability to influence engineers, productleadersand cross-functional stakeholders.