Senior Engineer - Machine Learning

Job Duties and ResponsibilitiesDeploy end-to-end pipelines in classical ML, deep learning, forecasting, and GenAI applications such as chatbots.Architect scalable and efficient data processing workflows using AWS cloud services such as S3, Glue, EMR, Lambda, SageMaker, AWS Step Functions, and Amazon Forecast.Establish and maintain CI/CD pipelines for automating model training, evaluation, and deployment processes, integrating with version control systems such as Git and CI/CD platforms like Jenkins or GitLab CI.Implement monitoring and logging solutions to track model performance, data drift, and other key metrics, leveraging tools such as Amazon CloudWatch, Elasticsearch, and AWS CloudTrail.Collaborate with DevOps teams to optimize infrastructure provisioning, configuration management, and deployment processes, ensuring scalability, reliability, and security of machine learning applications.Skills, Experience and RequirementsMust have:Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.4+ years of experience in machine learning engineering, data engineering, or related roles.Proficiency in Python and strong experience with machine learning libraries/frameworks such as PyTorch, Scikit-learn, and GenAI/Language models.Prior experience in deployment of ML models at scale.Solid understanding of data engineering concepts and experience with SQL and NoSQL databases.Experience working with AWS services such as S3, Glue, EKS, Lambda, SageMaker, Step Functions, CloudFormation.Good to have:Familiarity with DevOps practices and tools for infrastructure as code (e.g., Terraform), configuration management (e.g., Ansible), and CI/CD.Hands-on experience with containerization technologies (e.g., Docker) and container orchestration tools (e.g., Kubernetes).Benefits:Employee Stock PurchaseTerm InsuranceAccident InsuranceHealth InsuranceTraining ReimbursementGratuityMobile and Internet ReimbursementTeam Outings

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