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