Machine Learning Operations

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Machine Learning Operations Minimum 3 year(s) of experience is required Educational Qualification : BE Summary: As an AI/ML Engineer, you will be responsible for developing applications and systems that utilize AI to improve performance and efficiency. Your typical day will involve working with deep learning, neural networks, chatbots, and natural language processing to deliver impactful data-driven solutions. Roles & Responsibilities: - Tracking of multiple models, experiments for auditing purpose - Collaborate with data scientists for hyper parameter tuning and features maintenance. - Model management and versioning - Work with infra team, data scientists, business in establishing artifact lineage, model registry, governance. - Assist in testing, automated deployment - A pipeline for triggering retraining to detect model drift or data drift, trigger retraining. - Working knowledge of AWS Lambda, Sagemaker, AWS Step Functions Professional & Technical Skills: - Must To Have Skills: Strong experience in Machine Learning Operations. - Must To Have Skills: Proficiency in Python and related libraries such as NumPy, Pandas, and Scikit-learn. - Must To Have Skills: Experience with deep learning, neural networks, chatbots, and natural language processing. - Creation of CI,CD Pipelines for ML algorithms, training, prediction pipelines - Work with business, data scientists to ensure value realization, ROI on operationalization of models - Containerization, packaging of ML models - Endpoint creation for prediction - Solid understanding of statistical analysis and machine learning algorithms. - Experience with programming languages such as Python, R, and Java. - Experience with machine learning frameworks such as TensorFlow, Keras, and PyTorch. - Experience with data visualization tools such as Tableau or Power BI. - Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. - Experience with software development methodologies such as Agile or Scrum