Machine Learning Engineer

Job Title: Machine Learning Engineer Location: London, UK Employment Type: Full-time Department: Data Science / AI Engineering Role Overview We are seeking a highly skilled Machine Learning Engineer to design, build, and deploy scalable ML solutions that power intelligent products and services. You will work closely with data scientists, software engineers, and product teams to transform cutting-edge research into production-ready systems. Key Responsibilities • Model Development: Design, train, and optimize machine learning models for predictive analytics, NLP, computer vision, or recommendation systems. • Data Pipeline Engineering: Build and maintain robust data pipelines for feature extraction, transformation, and model training. • Deployment & Monitoring: Implement models into production environments using MLOps best practices (CI/CD, containerization, monitoring). • Performance Optimization: Continuously improve model accuracy, latency, and scalability. • Collaboration: Work cross-functionally with product managers and engineers to align ML solutions with business objectives. • Documentation & Compliance: Ensure proper documentation and adherence to data privacy and ethical AI standards. Required Skills & Qualifications • Education: Bachelor’s or Master’s in Computer Science, Data Science, Mathematics, or related field. • Programming: Strong proficiency in Python (TensorFlow, PyTorch, Scikit-learn), and experience with SQL. • ML Expertise: Solid understanding of supervised/unsupervised learning, deep learning, and model evaluation techniques. • Cloud & MLOps: Experience with AWS, GCP, or Azure; Docker/Kubernetes; MLflow or similar tools. • Data Handling: Familiarity with big data frameworks (Spark, Hadoop) and data versioning tools (DVC). • Soft Skills: Strong problem-solving, communication, and ability to work in agile teams. Preferred Qualifications • Experience with transformer-based models (e.g., BERT, GPT) and generative AI. • Knowledge of distributed training and GPU acceleration. • Familiarity with feature stores and real-time inference systems. Benefits • Competitive salary and bonus structure • Flexible working arrangements (hybrid model) • Professional development and training budget • Health and wellness benefi