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:
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Develop ML Platform to empower Data Scientists to perform end to end ML Ops.
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Work actively and collaborate with Data Science teams within Credit IT to design and develop end to end Machine Learning systems.
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Lead evaluation of design options, tools, and utilities to build implementation patterns for MLOps using VertexAI in the most optimal ways.
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Create solutions and perform hands-on PoCs.
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Develop end to end and scalable Generative AI solutions.
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Work with Suppliers, Google Professional Services, and other Consultants as required.
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Collaborate with program managers to plan iterations, backlogs, and dependencies across all workstreams to progress the program at the required pace.
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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.
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Bachelor’s degree in computer science or related field.
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8+ years of relevant work experience in solution, application, and ML engineering, DevOps with deep understanding of cloud hosting concepts and implementations.
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Proven expertise with Vertex AI.
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Very strong with programming in Python.
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Knowledge of SQL (Relational & Non-relational).
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5+ years of hands-on experience in Analytics, MLOps and Engineering Solutions for ML based models.
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Knowledge of enterprise frameworks and technologies.
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Strong in engineering design patterns, experience with secure interoperability standards and methods, engineering tools and processes.
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Strong in containerization using Docker/Podman.
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Strong understanding on DevOps principles and practices, including continuous integration and deployment (CI/CD), automated testing & deployment pipelines.
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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.
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Understanding of microservices architecture.
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Strong leadership, communication, interpersonal, organizing, and problem-solving skills.
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Strong in AI Engineering
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The candidate needs to possess necessary Cloud experience (necessary) - preferably in GCP.
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Demonstrated industry experience in developing end to end production grade AI/ML systems in both Traditional ML and Generative AI.
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Proficiency in Agentic AI frameworks.
Preferred:
Relevant certification in ML Engineering in GCP (GCP - Professional Machine Learning Engineer certification)
