Machine Learning Engineer

About DKatalis

DKatalis is a financial technology company with multiple offices in the APAC region. In our quest to build a better financial world, one of our key goals is to create an ecosystem-linked financial services business.

DKatalis is built and backed by experienced and successful entrepreneurs, bankers, and investors in Singapore and Indonesia who have more than 30 years of financial domain experience and are from top-tier schools like Stanford, Cambridge London Business School, JNU with more than 30 years of building financial services/banking experience from Bank BTPN, Danamon, Citibank, McKinsey & Co, Northstar, Farallon Capital, and HSBC.

 

About the role

We are seeking an experienced Machine Learning Engineer to collaborate with our data science team in rapidly designing, building, training, optimizing, deploying, and monitoring models in production environments. While our data scientists focus on data analysis, feature engineering, and model design, you will be responsible for providing and managing the infrastructure and tooling to ensure reliable deployment and operation in production.

Key Responsibilities:

• Build and operate the infrastructure to enable full-cycle Machine Learning Ops.
• Implement and manage feature stores, addressing challenges such as training-serving skew.
• Develop models supporting both online and offline inference, integrated into customer-facing product features requiring 99.9% uptime.
• Work closely with data engineering, product, software engineering, and DevOps teams.
• Balance technical and business concerns pragmatically, respecting long-term business objectives.

Your work will support various business capabilities, including:

• Digital banking product features (e.g., smart financial recommendations)
• Business operations optimization
• Growth, go-to-market, and customer engagement strategies
• Fraud detection and risk management functions
• Improving efficiency of technical operations within the business

Requirements:

Education and Background

• Bachelor's or Master's degree in Computer Science, Information Systems, or Engineering with a strong background in mathematics and statistics.

Technical Skills and Experience

• 3-5+ years of software engineering experience, with at least 2 years deploying machine learning models in production environments at scale.
• Familiarity with tools in the PyData ecosystem (Numpy, Scipy, pandas, scikit-learn, PyTorch, TensorFlow).
• Extensive experience with cloud technologies, particularly GCP (VertexAI, BigQuery).
• Proficiency with Kubernetes and container orchestration.
• Experience with Kubeflow or similar ML-specific orchestration tools.
• Experience with big data / distributed processing systems.

Knowledge and Expertise

• Demonstrated understanding of theoretical foundations underpinning machine learning and deep learning models, coupled with extensive hands-on experience solving real-world problems.
• Strong understanding of ML model monitoring, versioning, and A/B testing in production environments.

Soft Skills

• Excellent communication skills, both written and verbal.

Preferred Qualifications:

• Experience in the retail banking sector.
• Contributions to open-source ML or data engineering projects.
• Experience mentoring junior engineers or data scientists.
• Proven track record of improving ML infrastructure efficiency and scalability.

Stand-out Qualities

• Strong portfolio of relevant projects (e.g., well-maintained GitHub repositories)
• Active participation in the data science community (giving talks, attending meetups)
• Demonstrated thought leadership (blog posts, articles, or contributions to open-source projects)
• Self-motivated with a track record of initiating and completing projects

The ideal candidate will bring a wealth of experience in MLOps, demonstrating not just technical proficiency but also the ability to drive best practices, mentor team members, and contribute to the strategic direction of our ML infrastructure. We're looking for individuals who are not only technically proficient but also engaged with the broader data science community and capable of driving innovation within our team.