Job Description

Must have 7+ years of total IT experience in Data engineering and 3 years as MLOps engineer.Designing, developing, and implementing robust MLOps pipelines using industry-leading tools like MLflow, Apache Airflow etc.Working Experience with AWS and Databricks.Must have strong Proficiency in Python, Pyspark, SQL, Machine Learning, NLP, Deep Learning, DataBricks, AWS SageMaker for machine learning, AWS BedRock, AWS EC2,Lambda,S3 and Glue Tables.Experience in configuration management tools (Ansible, Terraform) and building CI/CD pipelines.Automating the entire machine learning lifecycle from data ingestion and model training to deployment and monitoring.Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.Collaborating closely with Data Scientists to understand the data science lifecycle and translate their needs into production-ready solutions.Data science models testing, validation and tests automation.Communicate with a team of data scientists, data engineers and architect, document the processes. Experience in Cloud native skills.Strong in ML Model Deployment, AI ML pipeline and model Monitoring and Participate in the development and maintenance of automated ML Ops pipelinesGood to have ML techniques and algorithms such as RNN,CNN,GNN,GAN etc.Technical proficiency and the ability to collaborate with technical teams.Excellent communication skills, both written and verbal.Analytical mindset with the ability to interpret data and metrics.

Virtusa