Senior Machine Learning Engineer

Overview

Senior Machine Learning Engineer to independently lead end-to-end ML model development and delivery within a programmatic DSP, from problem framing and pipelines through training, evaluation, deployment, and monitoring.

What you'll do

• Lead development of complex ML models for campaign selection and conversion prediction.
• Design and own scalable evaluation frameworks using offline metrics, backtesting, and online A/B analysis.
• Own end-to-end deployment of models and tools, including rollback plans and monitoring.
• Implement real-time monitoring to detect performance degradation and trigger responses.
• Improve inference latency while maintaining model performance.
• Coordinate cross-functional rollouts of model and architectural changes and protect production stability with guardrails and staged rollouts.
• Mentor junior ML contributors via code reviews, pairing, and structured guidance.

What you'll need

• 4–7 years of professional machine learning experience including end-to-end model development, deployment, and production monitoring.
• Bachelor’s or Master’s degree in Mathematics, Physics, Computer Science, or a related technical field.
• Experience leading complex model development with measurable business impact.
• Proficiency in Python and SQL, plus hands-on experience with big data tools (Spark) and ML libraries.
• Strong ML techniques experience including regression, classification, ranking, gradient-boosted trees, and neural architectures.
• Experience designing evaluation frameworks and diagnosing training/serving gaps, feature drift, and data quality issues.
• Strong grasp of probability, statistics, and experimental design including A/B testing and causal reasoning.

Nice to have

• Experience with system programming languages (C++, Rust) and contributing to the inference layer.
• Hands-on experience with online inference systems, gRPC/REST model endpoints, or streaming feature pipelines (Kafka/Flink).
• Ad-tech domain knowledge including auction dynamics and fraud detection.
• Experience with contextual bandits, multi-armed bandit frameworks, or reinforcement learning in production.
• Familiarity with MLOps tooling such as MLflow, Prefect/Airflow, and model registries.

Details

• Location: Bengaluru.