AI / Senior Machine Learning Engineer

WORKTUAL LIMITED; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

WORKTUAL LIMITED seeks a Senior Machine Learning Engineer to architect, design, train, and deploy machine learning models. The role focuses on low-latency, enterprise-grade software infrastructure powering 24/7 automated business tools across omnichannel streams.

Responsibilities include production MLOps, telemetry dashboards, data framework operationalization, and governance for audited environments such as finance and insurance.

Qualifications

• Bachelor’s or Master’s degree in a quantitative field.
• 5+ years of production ML experience.
• Proficient in Python and ML libraries (scikit-learn, TensorFlow, PyTorch).

Responsibilities

• Design, train, and optimize custom ML models and NLP pipelines.
• Build scalable ML production deployments with CI/CD.
• Monitor latency, throughput, and model accuracy degradation.
• Collaborate with data scientists to turn prototypes into features for CDP.
• Ensure data quality and feature engineering across large datasets.
• Document architecture and ensure compliance for audited environments.

Skills

Python development

Machine Learning

MLOps

Observability

Education

Bachelor’s or Master’s degree in Computer Science, ML, Data Analytics

Tools

MLflow

Prometheus

Grafana

ELK

Datadog

Job description

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).