Senior ML Engineer/ Data Engineer (Hadoop & Gen AI)

About the Company

We are a premium financial technology and enterprise solutions provider partnering with top-tier banking institutions to deliver next-generation data platforms. By blending cutting-edge artificial intelligence, robust data engineering, and highly disciplined banking governance architectures, we build secure, mission-critical systems that redefine modern digital banking. We focus heavily on engineering excellence, complex data systems modernization, and cultivating top-tier local technical talent in Singapore.

About the Job

We are looking for two (2) Senior Machine Learning & Data Engineers, In this high-impact role, you will bridge the gap between Big Data Engineering and advanced Machine Learning operationalization (MLOps).

You will design, build, and optimize scalable, real-time data streaming architectures and machine learning solutions across modern enterprise Hadoop ecosystems. This role requires an agile full-stack mindset—allowing you to collaborate closely with Data Science teams to operationalize multi-modal GenAI, NLP, and predictive models, while simultaneously building the internal full-stack engineering tools and ingestion pipelines that feed them.

Key Responsibilities

Streaming Architecture & Core Data Engineering

• Real-Time Platform Design: Architect, develop, and operate highly scalable, real-time streaming and data processing systems leveraging Hadoop ecosystem components including Apache Iceberg, Spark, Ozone, Trino, Hive, Ranger, Kafka, Flink, and NiFi.

• Multi-Modal Ingestion Pipelines: Build robust data ingestion and structural transformation frameworks using Java, Scala, Python, and shell scripting to process complex batch and real-time multi-modal datasets (including unstructured documents, audio, video, and imagery).

• Performance Optimization: Execute rigorous performance tuning, profiling, and workload optimization of heavy data applications distributed across Hadoop clusters to ensure maximum throughput and optimal resource utilization.

MLOps & Advanced Framework Operationalization

• Model Operationalization: Collaborate tightly with internal Data Science teams to scale, deploy, and operationalize advanced machine learning models via the Cloudera Machine Learning (CML) platform.

• ML Pipeline Integration: Integrate and maintain robust deployment pipelines utilizing mainstream ML libraries and platforms such as Spark MLlib, Scikit-learn, and XGBoost.

• GenAI Innovation: Support advanced Natural Language Processing (NLP), Natural Language Querying (NLQ), and Generative AI use cases by incorporating architectures like Hugging Face, TensorFlow, and Keras.

Full-Stack Internal Tooling

• Application Development: Design and build functional, full-stack internal engineering tools and control applications using Python, scripting, and modern UI frameworks (e.g., Flask, React).

Skills & Experience Required

Must-Have Skills (Mandatory for Skills Matching)

• Core Programming Foundations: Advanced, hands-on software development proficiency in Python, Java, or Scala.

• Big Data Ecosystems: Proven engineering experience designing pipelines with Apache Spark, Kafka, Hive, or next-gen storage backends like Apache Iceberg and Ozone.

• Machine Learning Engineering (MLOps): Demonstrated track record of deploying models into enterprise production via platforms like Cloudera Machine Learning (CML) or similar hybrid MLOps frameworks.

• Predictive ML Toolkits: Solid working knowledge of fundamental data science and ML building blocks: XGBoost, Scikit-learn, TensorFlow, or Keras.

Good-to-Have Skills

• Generative AI Lifecycle: Experience experimenting with or deploying Large Language Models (LLMs) and transformer pipelines via Hugging Face.

• Modern Frontend/Backend: Development experience with frontend frameworks (React) and backend micro-frameworks (Flask / FastAPI).

• Prior experience working within enterprise banking data lakes, secure financial data architectures, or heavily audited environments.

Qualifications & Engagement Terms

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a highly quantitative technical discipline.

• Strong analytical, communication, and cross-functional collaboration skills.

• Project Context: UOBI CADR Project.

• Positions Available: 2.

How to Apply

Please click on the 'apply' button to apply online. For more information, please reach out to Vievien Nathan.

EA License Number: 94C3609