Artificial Intelligence Engineer

Location - Menlo Park, CA ( Onsite DAY1 ) 4 days WFOAI Engineer with 6–10 years of experience designing and deploying scalable AI/ML solutions for AdTech platforms covering targeting, bidding, personalization, attribution, and real-time analytics.The role requires strong engineering fundamentals with hands-on ML model development, data pipelines, and real-time decision systems, leveraging modern distributed and cloud-based architectures.Key ResponsibilitiesDevelop and deploy AI/ML models for:Audience targeting & segmentationAd ranking & bidding optimizationAttribution & campaign performance modellingFraud detection & anomaly detectionBuild and optimize end-to-end ML pipelines:Data ingestion, feature engineering, training, and inferenceBatch & real-time model servingDesign real-time decisioning systems for high-throughput, low-latency environments.Collaborate with data engineers and architects to ensure:Scalable data pipelines (ETL/ELT, streaming)High-quality feature stores and model lifecycle managementDrive experimentation frameworks (A/B testing, causal inference) to continuously optimize performance metrics.Ensure privacy-aware and compliant AI solutions aligned with data governance frameworks.Required QualificationsBachelor's/Master's in Computer Science, Data Science, AI/ML, or related field.6–10 years of experience in AI/ML engineering / Data Science engineering roles.Strong programming skills in:Python (mandatory)Java or C++ (preferred)Hands-on experience in:ML frameworks (TensorFlow, PyTorch, XGBoost)Distributed processing (Spark, Flink)Streaming systems (Kafka)SQL & NoSQL databasesExperience building production-grade ML pipelines and scalable data systemsPreferred QualificationsExperience in AdTech / MarTech / Retail Media ecosystemsExposure to:Recommendation systemsReal-time bidding systemsExperimentation platforms / A/B testingFamiliarity with:Kubernetes, Docker, microservicesPrivacy and regulatory frameworks (GDPR, data compliance)