Description

You will build and maintain large-scale ML infrastructure and pipelines to enable model prediction and optimization.

Responsibilities

• Build and extend ML platforms to scale model training and deployment.
• Develop MLOps workflows in the cloud using AWS SageMaker.
• Construct data pipelines to ingest, process, and evaluate data for machine learning models.
• Partner with engineering teams to integrate and drive the adoption of model outputs.
• Design and maintain highly scalable distributed systems using open-source technologies.

Required Skills

• 8+ years of experience implementing big data solutions in the data analytics space.
• 2+ years of experience developing ML infrastructure and MLOps using AWS SageMaker.
• Proficiency in Object Oriented Programming with Java, Scala, or Python.
• Experience with SQL, Unix scripting, and Python's ML ecosystem (NumPy, Pandas, Scikit-learn, TensorFlow).
• Hands-on experience with distributed data processing frameworks like Apache Spark.
• Knowledge of data movement (ETL/ELT) and messaging/streaming technologies like AWS SQS and Kafka.
• Experience with relational and NoSQL databases including DynamoDB, EKS, and Graph databases.
• Competency with CI/CD tools, Git, and orchestration/DAG tools such as Airflow, Kubeflow, or AWS Step Functions.
• Strong background in Agile methodologies including Kanban and SCRUM.

Preferred Skills

• Experience with feature engineering and applied data science methods.
• Ability to manage data wrangling for structured, semi-structured, and unstructured data.

Key Skills

Education

Bachelor’s or Master’s Degree