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Design, build, test, and operate scalable, low-latency backend services in Java. Integrate applications with ML models and feature stores, implement resilience and observability, develop tests and SLAs, participate in incident response, and collaborate across software, ML, data, and infrastructure teams to ensure production reliability.

Build, test, and maintain scalable, low-latency Java backend services and microservices; integrate applications with ML models and feature stores; implement resilience, monitoring, testing, and observability; participate in incident response, reliability improvements, architecture and code reviews, and cross-team collaboration.

Design, build, test, and operate scalable, low-latency Java backend services and microservices. Integrate applications with ML models and feature stores, implement resilience patterns, automated testing, monitoring, tracing, and observability (Datadog/OpenTelemetry/Grafana/Prometheus). Optimize SLAs and performance, participate in incident response, RCA, architecture and code reviews, and collaborate with ML, data, infra, and product teams.

Design, build, test, and operate high-performance Java backend services and microservices integrated with ML models and feature stores. Ensure low-latency, high-availability production reliability, observability, resilience patterns, comprehensive testing, and incident response while collaborating across software, ML, data, infra, and product teams.

Design, build, and maintain AWS-based data enrichment pipelines (Spark/PySpark). Manage S3/Glue/Athena data platform, orchestrate with Airflow, perform deep data quality analysis, troubleshoot tracking and delivery issues, implement observability, and collaborate with global teams on data governance and pipeline optimization.

Design, build, and maintain scalable AWS-based data enrichment pipelines (Spark) and orchestrations (Airflow); use S3/Glue/Athena for storage, processing and querying; perform deep data quality analysis, implement validation/observability, troubleshoot tracking and delivery issues across hybrid systems, and collaborate on governance, lineage, and schema design to support media measurement and audience intelligence.

The Machine Learning Engineer role involves owning ML projects end-to-end, designing production ML systems, and leveraging AI tools to automate decision-making. Responsibilities include building robust pipelines, communicating with stakeholders, and mentoring team members, especially for Senior candidates.

Convert data‑science prototypes into production ML services; build and operate Databricks/Spark pipelines reading/writing Snowflake; manage full model lifecycle (MLflow, CI/CD, retraining, drift monitoring); deliver/version audience segments to ad‑tech partners; ensure scalability, cost efficiency, privacy, and mentor engineers.

Build and validate statistical and ML models to create, expand, and score audience segments from survey, purchase, and media data. Perform data fusion, propensity/lookalike modeling, audience measurement (reach, overlap, lift), and partner with ML engineering to productionize privacy-safe models. Propose product improvements and collaborate with US product and analytics teams.

Build and operate production-grade ML services and large-scale feature pipelines (Databricks/Spark, Snowflake, Azure). Own model lifecycle (MLflow, CI/CD, retraining, monitoring), activation/integration to DSPs/CDPs/clean rooms, and privacy-by-design patterns. Mentor engineers and set engineering standards.