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