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As a data scientist/AI Engineer you will work with various squads in the tribe on the automation of loan origination processes, portfolio monitoring, risk monitoring and risk modeling.
Job Responsibilities:
- Work on advanced analytics, Generative AI (GenAI) initiatives, and/or machine learning use cases.
- Leverage your expertise in AI/ML and cloud technologies to create impactful solutions, collaborate across teams, and contribute to innovations in our tech-driven environment.
- Use any data science tools to create value out of data.
- Keep yourself up to date and continuously search for new technologies in the field of Natural Language Processing & Machine Learning for finance.
- Combine both thinking of the future and a hands-on, right-now attitude.
- Work with internal and external customers to refine requirements of your solutions.
Qualifications:
- Bachelor's Degree in IT, Computer Science, or any related field
- Must be amenable to work in a hybrid work setup (2-3x a week onsite)
- Strong programming skills in Python with experience in delivering production grade code and package design.
- Proven expertise in Machine learning, NLP, and/or building LLM-based solutions.
- Hands-on experience with Python libraries like TensorFlow, PyTorch, Hugging Face Transformers, Scikit-learn, NumPy, Pandas, and Matplotlib/Seaborn.
- Proficiency in CI/CD pipelines, model monitoring, and observability tools.
- Experience with SQL and relational databases
- Experience in building and automating ML pipelines and deployment of models using Docker, Azure and Kubernetes.
- Experience with Model Lifecycle Management: Continuous Training, Continuous Monitoring (MLFlow, AirFlow, KubeFlow, etc.)
Other nice to have skills:
- Experience with Software Lifecycle Development: Version Control, Build and Deployment pipelines, Containerization.
- Experience in writing tests and following code quality standards and best practices (unit testing, E2E testing, pep8, flake8 pytest, etc.)
- Experience in working with and creating REST APIs
- Familiarity with modern architectures (microservices, event-driven architectures, integrations).
- Experience building or fine-tuning LLMs, implementing retrieval-augmented generation, and working with vector databases.
- Experience with any major cloud platform (GCP, AWS, Azure).
- Previous experience as data engineer or software engineer.
- Strong understanding of AI/ML ethics and responsible AI principle.
- Exposure to hybrid cloud architectures and multi-cloud strategies
As a data scientist/AI Engineer you will work with various squads in the tribe on the automation of loan origination processes, portfolio monitoring, risk monitoring and risk modeling.
Job Responsibilities:
- Work on advanced analytics, Generative AI (GenAI) initiatives, and/or machine learning use cases.
- Leverage your expertise in AI/ML and cloud technologies to create impactful solutions, collaborate across teams, and contribute to innovations in our tech-driven environment.
- Use any data science tools to create value out of data.
- Keep yourself up to date and continuously search for new technologies in the field of Natural Language Processing & Machine Learning for finance.
- Combine both thinking of the future and a hands-on, right-now attitude.
- Work with internal and external customers to refine requirements of your solutions.
Qualifications:
- Bachelor's Degree in IT, Computer Science, or any related field
- Must be amenable to work in a hybrid work setup (2-3x a week onsite)
- Strong programming skills in Python with experience in delivering production grade code and package design.
- Proven expertise in Machine learning, NLP, and/or building LLM-based solutions.
- Hands-on experience with Python libraries like TensorFlow, PyTorch, Hugging Face Transformers, Scikit-learn, NumPy, Pandas, and Matplotlib/Seaborn.
- Proficiency in CI/CD pipelines, model monitoring, and observability tools.
- Experience with SQL and relational databases
- Experience in building and automating ML pipelines and deployment of models using Docker, Azure and Kubernetes.
- Experience with Model Lifecycle Management: Continuous Training, Continuous Monitoring (MLFlow, AirFlow, KubeFlow, etc.)
Other nice to have skills:
- Experience with Software Lifecycle Development: Version Control, Build and Deployment pipelines, Containerization.
- Experience in writing tests and following code quality standards and best practices (unit testing, E2E testing, pep8, flake8 pytest, etc.)
- Experience in working with and creating REST APIs
- Familiarity with modern architectures (microservices, event-driven architectures, integrations).
- Experience building or fine-tuning LLMs, implementing retrieval-augmented generation, and working with vector databases.
- Experience with any major cloud platform (GCP, AWS, Azure).
- Previous experience as data engineer or software engineer.
- Strong understanding of AI/ML ethics and responsible AI principle.
- Exposure to hybrid cloud architectures and multi-cloud strategies