ML Engineer

As a ML Engineer, you will be part of a high performing team working on exciting opportunities in AI/ML. We are looking for 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.

• 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

• 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.