AI / Senior Machine Learning Engineer
| Dyddiad hysbysebu: | 13 Mai 2026 |
|---|---|
| Cyflog: | £60,000 i £65,000 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 12 Mehefin 2026 |
| Lleoliad: | E14 9NN |
| Gweithio o bell: | Ar y safle yn unig |
| Cwmni: | WORKTUAL LIMITED |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: | AI/SMLG 0001 |
Crynodeb
Core Purpose of the Role
The AI / Senior Machine Learning Engineer acts as the technical architect responsible for the design, training, optimization, and deployment of machine learning algorithms. This individual translates theoretical data models into robust, low-latency enterprise software infrastructure capable of powering 24/7 automated business tools across various communication streams
Detailed Duties & Responsibilities
• ML Model Architecture & Training: Build and scale custom Machine Learning algorithms and natural language pipelines .Focus on predictive analytics, text processing, intent interpretation, and omnichannel workflows
• Production MLOps Infrastructure: Own complete production deployment cycles, utilizing containerization mechanisms and robust Continuous Integration / Continuous Deployment (CI/CD) practices
• Telemetry & System Observability: Construct and scale live engineering dashboards to observe system latency, query throughput, model accuracy degradation, and data drift over time
• Operationalizing Data Frameworks: Collaborate closely with investigative Data Scientists to transform raw prototypes into enterprise-grade features integrated with Customer Data Platforms (CDP)
• Data Manipulation & Pipeline Quality: Oversee vast structured and unstructured communications data sets. Conduct feature engineering, data transformations, and comprehensive technical QA
• System Compliance & Governance: Generate exhaustive code documentation and architectural blueprints to maintain regulatory compliance for operations within highly audited environments, such as financial and insurance sectors
Required Qualifications & Education
• Minimum Education: Bachelor’s or Master’s Degree in Computer Science, Machine Learning, Data Analytics, or a highly related quantitative engineering field
Mandatory Experience & Skills Level
• Experience Required: Minimum of 5 years of proven experience building, testing, and deploying machine learning models directly into production environments).
• Tooling Proficiency: Advanced operational mastery of MLOps tools (such as MLflow) and observability systems (such as Prometheus, Grafana, ELK, or Datadog) .
• Languages & Libraries: Absolute proficiency in Python development alongside core data frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch, Pandas, NumPy, and advanced SQL querying).
The AI / Senior Machine Learning Engineer acts as the technical architect responsible for the design, training, optimization, and deployment of machine learning algorithms. This individual translates theoretical data models into robust, low-latency enterprise software infrastructure capable of powering 24/7 automated business tools across various communication streams
Detailed Duties & Responsibilities
• ML Model Architecture & Training: Build and scale custom Machine Learning algorithms and natural language pipelines .Focus on predictive analytics, text processing, intent interpretation, and omnichannel workflows
• Production MLOps Infrastructure: Own complete production deployment cycles, utilizing containerization mechanisms and robust Continuous Integration / Continuous Deployment (CI/CD) practices
• Telemetry & System Observability: Construct and scale live engineering dashboards to observe system latency, query throughput, model accuracy degradation, and data drift over time
• Operationalizing Data Frameworks: Collaborate closely with investigative Data Scientists to transform raw prototypes into enterprise-grade features integrated with Customer Data Platforms (CDP)
• Data Manipulation & Pipeline Quality: Oversee vast structured and unstructured communications data sets. Conduct feature engineering, data transformations, and comprehensive technical QA
• System Compliance & Governance: Generate exhaustive code documentation and architectural blueprints to maintain regulatory compliance for operations within highly audited environments, such as financial and insurance sectors
Required Qualifications & Education
• Minimum Education: Bachelor’s or Master’s Degree in Computer Science, Machine Learning, Data Analytics, or a highly related quantitative engineering field
Mandatory Experience & Skills Level
• Experience Required: Minimum of 5 years of proven experience building, testing, and deploying machine learning models directly into production environments).
• Tooling Proficiency: Advanced operational mastery of MLOps tools (such as MLflow) and observability systems (such as Prometheus, Grafana, ELK, or Datadog) .
• Languages & Libraries: Absolute proficiency in Python development alongside core data frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch, Pandas, NumPy, and advanced SQL querying).