Data Science Professional

What I’ll be doing – your accountabilities

Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning and deep learning code, pipelines.Manage data and model versioning, training, tuning, serving, experiment and Build and maintain tools and infrastructure for data processing for AI/ML development initiatives. evaluation tracking dashboards. 

Job Responsibilities

•    Automating machine learning model deployment
•    Bridges the gap between ML development and operations .Ensures smooth deployment and management of ML models in production
•    Streamlines ML lifecycle- data preparation and training to deployment and monitoring.
•    Automating CI/CD pipelines to account for data, code, and model changes
•    Enhances reproducibility and scalability of ML workflows.
•    Setting up automated model retraining 
•    Proactively identify hidden problems and patterns in data and uses these insights to drive improvements
•    Contribute to software engineering communities of practice and events that explore new and emerging technologies
•    Adds to team culture of diversity, equity, inclusion, and respect

Skills required for the job

•    5 years experience building production-quality software.
•    Experience deploying machine learning models into production environment.
•    Strong DevOps, and ML background with Cloud platforms
•    Fluency in Python and SQL
•    Comfort with Linux administration
•    Experience building custom integrations between cloud-based systems using APIs
•    Experience in containerization and orchestration (such as Docker, Kubernetes)
•     Experience with ML training/retraining, Model Registry, ML model performance measurement using ML Ops open source frameworks.
•    Experience building/operating systems for data extraction, ingestion and processing of large data sets
•    Experience with CI/CD
•    Experience in Docker and Kubernetes
•    Familiarity with one or more MLOps frameworks  like(MLFLow,KubeFlow, Airflow,etc.)
•    Experience in tools like github/gitlab
•     Experience working on large scale, distributed systems