Description

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.

Responsibilities

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 Skilled 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.

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.
- 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)

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