Senior Machine Learning Engineer

What you will do as a Senior ML Engineer • Design, build, and optimise machine learning models, including NLP, computer vision, and predictive analytics. • Own the ML lifecycle from data preparation through training, evaluation, and deployment. • Implement and maintain MLOps workflows for continuous integration and delivery of ML models. • Collaborate with Data Engineers and DevOps teams to ensure production readiness and scalability. • Contribute to architecture decisions for ML pipelines and data flows. • Apply secure coding and configuration practices in line with compliance standards. • Mentor junior engineers and share best practices across the team. • Support innovation by researching emerging ML techniques and tools. What you’ll bring • Proven experience developing and deploying machine learning models in production environments. • Proven experience with the OpenCV framework and various object detection models, including YOLO, RCNN, and Vision models, along with a clear understanding of when to apply each model. • Proficiency with object detection concepts. Experience in video analysis, particularly optical flow and object tracking. • Solid knowledge of Optical Character Recognition (OCR) models, with the ability to fine-tune these models using custom datasets. • An understanding of how to measure the accuracy of text extractions through metrics like Character Error Rate (CER) and Word Error Rate (WER) is also crucial. • Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch). • Understanding of ML architectures, hyperparameter tuning, and performance optimisation. • Experience with MLOps tools and CI/CD pipelines. • Familiarity with data engineering concepts (ETL, data pipelines, SQL). • Ability to analyse complex data and communicate insights effectively. • Strong problem-solving skills and attention to detail. • Excellent collaboration and stakeholder engagement skills. Core areas (must have): • ML Development Expertise: Hands-on experience building and deploying ML models. • Lifecycle Ownership: Ability to manage ML workflows from design to production. • Tool Proficiency: Skilled in Python, ML frameworks, and MLOps tooling. • Data Engineering Awareness: Understanding of data pipelines, warehousing, and integration. • Governance & Compliance: Familiarity with secure coding and quality assurance standards. • Collaboration & Mentoring: Ability to work across teams and support junior engineers. • Continuous Improvement: Commitment to learning and applying emerging ML techniques. • Desirable: • Experience with cloud platforms (AWS) and containerisation (such as Docker, Podman, Kubernetes). • Exposure to big data technologies (Spark, Hadoop) and Apache tools. • Knowledge of NLP, computer vision, and deep learning architectures. • Familiarity with Agile and DevOps practices. • STEM degree or equivalent experience in AI, Data Science, or related fields. • Industry certifications (e.g., TensorFlow Developer, AWS Machine Learning Specialty). • Experience working in secure or regulated environments. SC clearance is manadaorty for this role