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.

• 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.