ML Engineer

Synechron; City Of London; On-site; GBP 50,000 - 80,000; Full time

Job summary

A leading technology firm is seeking a Machine Learning Developer skilled in Spark ML to design and optimize models for large-scale data processing. This role involves developing machine learning solutions, analyzing data for insights, and collaborating with engineers to enhance ML workflows, fostering advancements in the field.

Qualifications

• Proficiency in Apache Spark and Spark MLlib for ML tasks.
• Experience with distributed systems like Hadoop.
• Strong understanding of predictive modeling techniques.

Responsibilities

• Develop and implement machine learning models using Spark ML.
• Design and optimize training and inference pipelines for distributed systems.
• Monitor and troubleshoot deployed models for reliability.

Skills

Apache Spark

Predictive modeling

Data preprocessing

Feature engineering

Distributed computing

Education

Degree in Computer Science or related field

Tools

Apache Spark

Hadoop

Python

Scala

Java

Job description

Synechron are seeking a skilled Machine Learning Developer with expertise in Spark ML, predictive modeling, and deploying training and inference pipelines on distributed systems such as Hadoop. The ideal candidate will design, implement, and optimize machine learning solutions for large-scale data processing and predictive analytics.

Responsibilities:

Develop and implement machine learning models using Spark ML for predictive analytics.

Design and optimize training and inference pipelines for distributed systems (e.g., Hadoop).

Process and analyze large-scale datasets to extract meaningful insights and features.

Collaborate with data engineers to ensure seamless integration of ML workflows with data pipelines.

Evaluate model performance and fine-tune hyperparameters to improve accuracy and efficiency.

Implement scalable solutions for real-time and batch inference.

Monitor and troubleshoot deployed models to ensure reliability and performance.

Stay updated with advancements in machine learning frameworks and distributed computing technologies.

• Proficiency in Apache Spark and Spark MLlib for machine learning tasks.

• Strong understanding of predictive modeling techniques (e.g., regression, classification, clustering).

• Experience with distributed systems like Hadoop for data storage and processing.

• Proficiency in Python, Scala, or Java for ML development.

• Familiarity with data preprocessing techniques and feature engineering.

• Knowledge of model evaluation metrics and techniques.

• Experience with deploying ML models in production environments.

• Understanding of distributed computing concepts and parallel processing.