DescriptionAs 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.
Responsibilities- 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.
Qualifications- Bachelors 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
DescriptionAs 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 keepin...
DescriptionAs 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.
Responsibilities- 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.
Qualifications- Bachelors 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
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