AI / Senior Machine Learning Engineer


Company:	WORKTUAL LIMITED
Salary:	from £60,000 a year
Hours:	Full-time
Location:	London
Working pattern:	On-site
Job type:	Permanent
Posting date:	29 Oct 2026
Closing date:	15 Nov 2026

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Summary

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

Essential requirements

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).

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