Real-Time ML Engineer - Scalable Data & Models

Technopals Pte.; Carlisle; On-site; GBP 89,000 - 124,000; Full time

Job summary

Technopals Pte.Ltd. is seeking a data engineering professional to design and build scalable real-time data systems on the Hadoop ecosystem, collaborating with data scientists to operationalize ML models using CML.

The role spans data ingestion, transformation, and full-stack tooling with Java, Spark, Python, Flask, and React, emphasizing performance tuning and efficient resource use across batch and streaming workloads.

Qualifications

• Experience building scalable real-time data systems on Hadoop stack.
• Expertise with ML platforms and Python ML libraries (scikit-learn, XGBoost).
• Strong data ingestion and transformation framework development skills.
• Proficiency in full-stack development using Python and modern web frameworks.
• Ability to collaborate with data scientists to operationalize ML models.
• Experience with performance tuning of Hadoop-based data applications.

Responsibilities

• Design and develop highly scalable Real-time systems with Hadoop ecosystem.
• Ingest and transform multi-model data (image, audio, video, documents) using Java, Spark, Python.
• Develop full-stack applications and tools using Python, shell, Flask/React.
• Collaborate with data scientists to operationalize ML models using CML.
• Tune performance of Hadoop-based data applications for optimal resource use.

Skills

ML platforms

Hadoop ecosystem

Data ingestion frameworks

Full-stack Python/JS

Cloudera Machine Learning

Performance tuning on Hadoop

Tools

Java

Python

Spark

Shell scripting

Flask

React

CML

Kafka

Flink

Nifi

Job description

Technopals Pte.Ltd. is seeking a data engineering professional to design and build scalable real-time data systems on the Hadoop ecosystem, collaborating with data scientists to operationalize ML models using CML.

The role spans data ingestion, transformation, and full-stack tooling with Java, Spark, Python, Flask, and React, emphasizing performance tuning and efficient resource use across batch and streaming workloads.