Senior Machine Learning Platform Engineer

What you'll do

• Design and maintain scalable ML pipelines and platforms for ingestion, feature engineering, training, evaluation, inference, and deployment
• Build and optimize large-scale data workflows using distributed systems (Spark, Hadoop, Kafka, Snowflake) to support analytics and model training
• Develop frameworks for experiment tracking, automated reporting, and observability to monitor model health, drift, and anomalies
• Work with industry-standard ML efficiency tools to optimize training workloads, accelerate experiments, and monitor performance at scale
• Provide reusable components, SDKs, and APIs that empower teams to leverage AI insights and ML models effectively
• Drive CI/CD, workflow orchestration, and infrastructure-as-code practices for ML jobs, ensuring reliability and reproducibility
• Partner cross-functionally with Product, Data Science, and Engineering teams to align ML infrastructure with business needs
• Stay ahead of emerging trends in Generative AI, ML Ops, and Big Data to introduce best practices and next-gen solutions
• Work with petabyte-scale datasets and billions of transactions, powering global adtech
• Apply AI/ML to deal troubleshooting, competitive intelligence, benchmarking, forecasting, and actionable insights
• Build advanced frameworks such as RAG systems, reinforcement learning strategies, and embedding platforms
• Convert business challenges into ML products, pioneering industry-first solutions
• Gain hands-on exposure to GPU/accelerated computing, Triton inference, and modern ML Ops frameworks
• Advance your career with a clear growth path into applied ML engineering and research
• Be part of a culture that values experimentation, thought leadership, and cross-functional collaboration

Requirements

• Four plus years of hands-on experience with petabyte-scale datasets and distributed systems
• Proficiency in SQL, Python (pandas, NumPy), and analytics/BI tools for data exploration and monitoring
• Exposure to graph-based systems (Neo4j), RAG approaches, or embedding platforms
• Experience building recommendation systems, personalization pipelines, or anomaly detection frameworks
• Familiarity with BI tools such as Looker or Grafana
• A passion for applied AI/ML and eagerness to bring research ideas into production
• Strong expertise with Spark, Hadoop, Kafka, and data warehousing (Snowflake, SparkSQL)
• Experience with CI/CD, Docker, Kubernetes, Airflow/MLflow, and experiment tracking tools
• Skilled in Python/Scala/Java for data-intensive applications
• Strong analytical skills and ability to debug complex data/ML pipeline issues
• Excellent communication and teamwork skills in cross-functional settings
• Bachelor’s degree in engineering (CS / IT) or equivalent degree from well-known Institutes / Universities

Tech stack

Benefits