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Data Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods and models. What’s more, we are in collaboration with leading academics, industry experts and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.
Job Responsibility:
Build and maintain full‑stack applications that integrate advanced ML models, large‑scale data pipelines, and real‑time/streaming data flows
Collaborate with data scientists to fine‑tune and productionize LLMs and other GenAI models using MLOps best practices
Design and implement highly scalable APIs and microservices to support AI/ML workloads and distributed batch inferencing
Develop and orchestrate Big Data workflows using Apache Spark, PySpark, Kafka, Airflow, and related technologies
Requirements:
Hands-on experience with MLOps tools like MLflow, Kubeflow, or SageMaker
Solid understanding of machine learning concepts, model lifecycle, and deployment strategies
Strong experience in Big Data processing using Apache Spark, PySpark, and distributed computation frameworks
Experience building and managing Kafka-based streaming pipelines and event-driven architectures
Solid understanding of Airflow or similar workflow orchestration tools for ETL/ELT and ML pipelines
6 to 8 years of related experience
Bachelor’s degree
What we offer:
Comprehensive Healthcare Programs
Award Winning Financial Wellness Tools and Resources
Generous Leave of Absence for New Parents and Caregivers