Location - Menlo Park, CA ( Onsite DAY1 ) 4 days WFO
If you want to know about the requirements for this role, read on for all the relevant information.
AI 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.
end-to-end ML pipelines :
Data ingestion, feature engineering, training, and inference
Batch & real-time model serving
Design
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 management
Drive
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 Qualifications
Bachelor’s/Master’s in Computer Science, Data Science, AI/ML, or related field. xsgimln
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 databases
Experience building
production-grade ML pipelines and scalable data systems
Preferred Qualifications
Experience in
AdTech / MarTech / Retail Media ecosystems
Exposure to:
Recommendation systems
Real-time bidding systems
Experimentation platforms / A/B testing
Familiarity with:
Kubernetes, Docker, microservices
Privacy and regulatory frameworks (GDPR, data compliance)