AI and Machine Learning Engineer

Tech Brummies Consulting Ltd; Birmingham; On-site; GBP 50,000 - 70,000; Full time

Job summary

A leading tech consulting firm in the United Kingdom is looking for an AI/Machine Learning Engineer to design and deploy advanced AI systems. Responsibilities include developing scalable ML models, collaborating with data teams, and ensuring data management and compliance. Ideal candidates will have programming skills, familiarity with ML frameworks, and a Bachelor's or Master's degree in a relevant field. Join us to innovate and contribute to groundbreaking solutions in AI.

Qualifications

• Strong programming skills, especially in Python, with familiarity in Java or R.
• Experience with machine learning frameworks and libraries like TensorFlow and PyTorch.
• Deep understanding of mathematics, probability, statistics, and algorithms.
• Knowledge of software architecture, system design, and testing best practices.
• Familiarity with cloud platforms such as AWS, Azure, or GCP and MLOps tools.
• Excellent analytical, problem-solving, communication, and teamwork skills.

Responsibilities

• Design and develop robust, scalable AI and machine learning systems.
• Research, implement, train, and retrain ML algorithms and models.
• Collaborate to build optimized data pipelines and manage data collection.
• Deploy models into production environments and manage infrastructure.
• Run tests and monitor deployed models for performance and bias.
• Work with teams to translate business problems into ML solutions.
• Document workflows and results while ensuring compliance with policies.
• Stay updated with the latest AI advancements and research.

Skills

Programming skills

Strong analytical skills

Problem-solving abilities

Critical thinking skills

Communication skills

Teamwork abilities

Education

Bachelor's or Master's degree in Computer Science, Data Science, or related field

Tools

TensorFlow

PyTorch

scikit-learn

Keras

AWS

Azure

GCP

MLflow

SageMaker

Vertex AI

Job description

An AI/Machine Learning (ML) Engineer designs, builds, and deploys intelligent, self-learning systems that automate processes and generate insights from data. They bridge the gap between data science (model creation) and software engineering (production-level implementation).

Roles and Responsibilities

• System Design & Development: Design and develop robust, scalable AI and machine learning systems and deep learning applications.
• Model Implementation: Research, implement, train, and retrain appropriate ML algorithms and models using frameworks like TensorFlow or PyTorch.
• Data Management: Collaborate with data engineers to build optimized and reliable data pipelines, manage data collection, and perform data preprocessing/feature engineering to ensure data quality.
• Deployment & Operations (MLOps): Deploy models into production environments, build APIs and microservices for integration with other applications, and manage the infrastructure needed for scaling.
• Testing & Monitoring: Run tests and experiments to analyse data and fine-tune models for optimal performance; monitor deployed models for performance degradation, bias, or "drift," and implement retraining strategies.
• Collaboration & Communication: Work closely with data scientists, software engineers, and product managers to translate business problems into ML solutions and communicate complex technical concepts to non-technical stakeholders.
• Documentation & Ethics: Document workflows, parameters, and results, while ensuring compliance with data governance, security, privacy, and ethical policies.
• Innovation: Stay updated with the latest AI advancements and research, continuously seeking improvements for existing infrastructure and systems.

Essential Skills and Qualifications

• Technical Proficiency: Strong programming skills, especially familiarity with other languages like Java or R or Phyton.
• ML Frameworks/Libraries: Experience with machine learning frameworks and libraries (e.g., scikit-learn, Keras, PyTorch, TensorFlow).
• Foundational Knowledge: Deep understanding of mathematics, probability, statistics, algorithms, and data structures.
• Software Engineering Principles: Knowledge of software architecture, system design, and best practices for building production-ready code (including version control, testing).
• Cloud & MLOps Tools: Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tools (e.g., MLflow, SageMaker, Vertex AI) for deployment and scaling.
• Soft Skills: Strong analytical, problem-solving, and critical thinking skills, along with excellent communication and teamwork abilities.
• Education: A Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field is typically required.