Company Name

Ford ( Chennai )

Job Description

• 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.

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