ML Engineer

DEVI TECHNOLOGIES LIMITED; Birmingham; On-site; GBP 40,000 - 60,000; Full time

Job summary

A technology solutions company based in the United Kingdom is seeking a Machine Learning Engineer to design, develop, and implement machine learning models for various applications. The role requires proficiency in working with large data sets, deploying models, and collaborating with cross-functional teams to define project goals. Candidates should be familiar with algorithms, APIs, and tools like TensorFlow and Docker. A commitment to documenting processes and adhering to ethical AI practices is essential.

Qualifications

• Experience in designing and implementing machine learning models.
• Familiarity with data privacy laws and ethical AI practices.
• Strong knowledge of data transformation techniques.

Responsibilities

• Clean and preprocess large datasets from various sources.
• Choose and optimize machine learning algorithms for performance.
• Deploy models into production and monitor their performance.
• Document model development and adhere to AI standards.

Skills

Machine Learning

Data Preprocessing

Model Optimization

API Integration

Documentation

Tools

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

AWS

GCP

Azure

Job description

Overview

Design, develop, and implement machine learning models for various applications such as classification, regression, clustering, and recommendation systems.

Responsibilities

• Clean, preprocess, and transform large datasets; work with structured and unstructured data from diverse sources.
• Choose appropriate algorithms based on business goals, and optimize models for performance, scalability, and accuracy.
• Deploy models into production environments and integrate with existing systems via APIs or pipelines using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure).
• Monitor model performance post-deployment and retrain/update models as needed to maintain accuracy and relevance.
• Work closely with data scientists, software engineers, product managers, and stakeholders to define project goals and deliverables.
• Stay up to date with the latest ML/AI research and incorporate cutting-edge techniques and frameworks when applicable.
• Utilize ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, XGBoost, and others for model building and experimentation.
• Ensure machine learning solutions are scalable and optimized for performance on large datasets or real-time systems.
• Maintain clear documentation of model development, data workflows, and experiments for reproducibility and future reference.
• Adhere to data privacy laws, model explainability standards, and ethical AI practices in all stages of ML development.