Staff / Senior Machine Learning Engineer

About the role

We are hiring a Staff Machine Learning Engineer to help us turn data science work into a reliable, customer-facing product, and we will also consider strong Senior candidates. This is a hands-on senior IC role. At Staff level, you will set technical direction for how we ship and operate ML in production; at Senior level, you will own significant slices of that work and help shape direction alongside the team. About the team and the work You'll join our Data Science team to build the ML systems powering a digital bank serving millions of customers. Current work spans: A broad portfolio of production models across fraud, collections, scoring, and customer-facing features, with active investment in shared monitoring, retraining, and feature infrastructure. Real-time credit and risk decisioning in the loan origination flow. A multi-cloud LLM platform under active build, supporting LLM-backed product features such as intelligent document understanding, agentic assistants, and conversational banking. The constraints are real (regulated environment, customer money, data residency requirements), and the platform is still being shaped, so there is genuine build work to do, not just maintenance.

Responsibilities

• Own and evolve our full-stack ML platform (the unified system through which every model is built, served, monitored, and retrained) so that data scientists ship business value faster and more reliably.
• Compress time-to-production for new models, turning research into customer-facing features in weeks rather than quarters, with MLOps practices that make each launch routine rather than bespoke.
• Protect the business from model risk by using the platform's drift detection, model monitoring, and A/B and canary deployment capabilities to catch degradations before they affect customers or revenue.
• Unlock new product surfaces (credit decisioning, fraud, personalization, LLM-backed experiences) by offering real-time and batch inference with the latency, reliability, and feature store consistency they require.
• Reduce the cost and effort of running ML in production by using standardized CI/CD for ML, model versioning, reproducible training, and clear operational ownership.
• Mentor engineers, partner with data, product, and platform teams, and set the standards that shape how DKatalis does ML.
• Your work will support various business capabilities, including:
• Digital banking and financial product features (e.g., smart financial recommendations, personalization)
• Credit and risk scoring (real-time and batch)
• Fraud detection and risk management
• Growth, go-to-market, and customer engagement strategies
• Improving the efficiency of technical and business operations

Requirements

• Technical Skills and Experience
• We expect strong software engineering and ML foundations, plus depth in one of two MLE specializations. If you have most of the following, please apply - we don't expect any single candidate to tick every box.
• 5+ years (Senior) or 8+ years (Staff) of software engineering, with substantial time shipping production machine learning.
• Production-grade Python and software engineering practice: clean code, typing, testing (pytest), code review, and systems design for end-to-end ML pipelines (batch and streaming, capacity and latency planning, API design).
• Cloud and infrastructure fluency on GCP (Vertex AI, BigQuery, GKE, Cloud Run, Pub/Sub) or AWS (primarily SageMaker and Bedrock). We mostly use GCP. Comfort with Kubernetes and infrastructure-as-code (Terraform) is required.
• Strong SQL and data warehouse skills, with attention to query and storage optimization (BigQuery in particular).
• Depth in one of the two MLE directions:
• Applied ML and algorithms: model selection, evaluation, feature engineering, and familiarity with some of the PyData stack (e.g., pandas, scikit-learn, PyTorch, or TensorFlow).
• MLOps and platform / SRE: workflow orchestration (e.g., Kubeflow, Vertex Pipelines, Airflow), CI/CD for ML, observability, and SLOs.
• Production ML Expertise
• Depth in whichever direction matches your specialization:
• Applied ML and algorithms:
• ML fundamentals: supervised learning algorithms, evaluation metrics, validation strategy, overfitting, and judgment on when deep learning is the right tool.
• Feature engineering and handl

Additional Information

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.

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