About this role
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. The candidate should also be proficient with AI literacy including Gen AI.
The ML Engineer is expected to develop AI/ML Engineering Solutions, perform DevOps and work closely with other stakeholders (ML Engineers, Data Scientists, and Data Engineers) with key responsibilities to:
• 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. • Develop end to end and scalable Generative AI solutions. • 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.
• 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. • Proven expertise with Vertex AI. • Very strong with programming in Python. • Knowledge of SQL (Relational & Non-relational). • 5+ years of hands-on experience in 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 • The candidate needs to possess necessary Cloud experience (necessary) - preferably in GCP. • Demonstrated industry experience in developing end to end production grade AI/ML systems in both Traditional ML and Generative AI. • Proficiency in Agentic AI frameworks. Preferred: Relevant certification in ML Engineering in GCP (GCP - Professional Machine Learning Engineer certification)