Virtusa
Website:
virtusa.com
Job details:
Key Responsibilities
Design, develop, train, and deploy machine learning and deep learning models for real-world business use cases
Build end-to-end ML pipelines including data ingestion, feature engineering, model training, evaluation, and deployment
Implement and manage ML workflows using AWS services such as SageMaker, S3, EC2, Lambda, Glue, and Step Functions
Deploy models as scalable APIs using Docker, REST endpoints, and CI/CD pipelines
Monitor model performance, drift, and retraining strategies in production
Collaborate with data engineering teams to ensure high-quality, reliable data pipelines
Optimize model performance, cost, and scalability on AWS infrastructure
Ensure solutions meet security, compliance, and governance standards
Required Skills & Experience
3+ years of experience in Machine Learning / AI development
Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch)
Hands-on Experience With AWS ML Stack, Including
Amazon SageMaker (training, endpoints, pipelines)
S3, EC2, IAM, Lambda
Glue, Athena, Redshift (nice to have)
Experience with model deployment and MLOps practices
Solid understanding of supervised, unsupervised, and deep learning techniques
Experience with REST APIs, Docker, and Git-based version control
Strong problem-solving and communication skills
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