Job
Description
As a ML Engineer, you will be part of a high performing team working on exciting opportunities in AI/ML. You are expected to be a highly skilled, technical, hands-on ML engineer with a solid background in building end-to-end AI/ML applications, exhibiting a strong aptitude for learning and keeping up with the latest advances in AI/ML. Responsibilities: - Develop ML Platform to empower Data Scientists to perform end to end ML Ops. - Work actively and collaborate with Data Science teams within Credit IT to design and develop end to end Machine Learning systems. - Lead evaluation of design options, tools, and utilities to build implementation patterns for MLOps using VertexAI in the most optimal ways. - Create solutions and perform hands-on PoCs. - Work with Suppliers, Google Professional Services, and other Consultants as required. - Collaborate with program managers to plan iterations, backlogs, and dependencies across all workstreams to progress the program at the required pace. - Collaborate with Data/ML Engineering architects, SMEs, and technical leads to establish best practices for data products needed for model training and monitoring considering regulatory policy and legal compliance. - Develop end to end and scalable Generative AI solutions. Qualifications: - Bachelor's degree in computer science or related field. - 8+ years of relevant work experience in solution, application, and ML engineering, DevOps with deep understanding of cloud hosting concepts and implementations. - 5+ years of hands-on experience in Risk Analytics, MLOps and Engineering Solutions for ML based models. - Knowledge of enterprise frameworks and technologies. - Strong in engineering design patterns, experience with secure interoperability standards and methods, engineering tools and processes. - Strong in containerization using Docker/Podman. - Strong understanding on DevOps principles and practices, including continuous integration and deployment (CI/CD), automated testing & deployment pipelines. - Good understanding of cloud security best practices and be familiar with different security tools and techniques like Identity and Access Management (IAM), Encryption, Network Security, etc. - Understanding of microservices architecture. - Strong leadership, communication, interpersonal, organizing, and problem-solving skills. - Strong in AI Engineering. As a ML Engineer, you will be part of a high performing team working on exciting opportunities in AI/ML. You are expected to be a highly skilled, technical, hands-on ML engineer with a solid background in building end-to-end AI/ML applications, exhibiting a strong aptitude for learning and keeping up with the latest advances in AI/ML. Responsibilities: - Develop ML Platform to empower Data Scientists to perform end to end ML Ops. - Work actively and collaborate with Data Science teams within Credit IT to design and develop end to end Machine Learning systems. - Lead evaluation of design options, tools, and utilities to build implementation patterns for MLOps using VertexAI in the most optimal ways. - Create solutions and perform hands-on PoCs. - Work with Suppliers, Google Professional Services, and other Consultants as required. - Collaborate with program managers to plan iterations, backlogs, and dependencies across all workstreams to progress the program at the required pace. - Collaborate with Data/ML Engineering architects, SMEs, and technical leads to establish best practices for data products needed for model training and monitoring considering regulatory policy and legal compliance. - Develop end to end and scalable Generative AI solutions. Qualifications: - Bachelor's degree in computer science or related field. - 8+ years of relevant work experience in solution, application, and ML engineering, DevOps with deep understanding of cloud hosting concepts and implementations. - 5+ years of hands-on experience in Risk Analytics, MLOps and Engineering Solutions for ML based models. - Knowledge of enterprise frameworks and technologies. - Strong in engineering design patterns, experience with secure interoperability standards and methods, engineering tools and processes. - Strong in containerization using Docker/Podman. - Strong understanding on DevOps principles and practices, including continuous integration and deployment (CI/CD), automated testing & deployment pipelines. - Good understanding of cloud security best practices and be familiar with different security tools and techniques like Identity and Access Management (IAM), Encryption, Network Security, etc. - Understanding of microservices architecture. - Strong leadership, communication, interpersonal, organizing, and problem-solving skills. - Strong in AI Engineering.
