Senior ML Engineer (Data Scientist)

We’re a Series A MLOps and enterprise AI platform company helping organizations deploy, manage, and monitor machine learning models at scale. Our Kubernetes-native infrastructure and model governance tooling are trusted by enterprise customers, and we’re now investing heavily in predictive product simulations, agentic AI capabilities, and next-generation data science infrastructure., * Build and optimize data pipelines (ETL/ELT) across SQL/NoSQL systems, ensuring reliability and quality of large-scale event and log data.

• Apply statistical modeling, causal inference, and ML to analyze user behavior, design experiments, and generate actionable insights.
• Develop predictive, generative, and clustering models - including embeddings, anomaly detection, and time-series - to power simulations and personalization features.
• Collaborate with a multidisciplinary team of GenAI experts, behavioral scientists, and ML engineers to create synthetic personas and deliver customer-ready reports and presentations.
• Deploy and scale models in cloud environments (AWS, GCP, and/or Azure) using containerized workflows with Docker and Kubernetes.
• Design and maintain monitoring and evaluation pipelines to track model performance, detect drift, and ensure fairness and reproducibility.
• Scale data science infrastructure end-to-end - from ingestion pipelines through to experimentation frameworks.

• 3+ years of experience as a Data Scientist or Machine Learning Engineer.
• Hands-on experience building and deploying ML models with PyTorch and TensorFlow; strong proficiency in Python.
• Strong ML/DS fundamentals with the ability to translate research insights into product decisions., * Experience with distributed data processing frameworks such as Spark, Dask, or Ray.
• Proficiency with containerization and orchestration tools - Docker and Kubernetes.
• Proven experience deploying data science and ML workloads on cloud platforms (AWS, GCP, and/or Azure).

Nice to have:

• Experience designing and implementing scalable data pipelines and experimentation frameworks.
• Background in causal inference, synthetic data generation, or behavioral modeling.
• Familiarity with model monitoring, drift detection, and explainability tooling.