AI/ML Platform Engineer

JOB TITLE

AI/ML Platform Engineer VACANCY NO

VN469 BASIS

Permanent FULL TIME / PART TIME

Full Time ENTITY

Aventum Group Management Services Ltd LOCATION CITY

London JOB DETAILS

Role Summary

As an AI / ML Platform Engineer, you will design, build, and operate the Azure‑based data, streaming, and feature‑store platforms that power all machine learning and intelligent decisioning across Aventum’s digital suite. This is a pivotal role in establishing a trusted, real‑time, ML‑ready data and platform layer, enabling scalable model development, deployment, and governance across underwriting, pricing, claims, and analytics. Our digital suite is a governed decisioning system that unifies insurance from capacity to close turning documents and data into evidence, and transforming rules and models into consistent, explainable, and auditable outcomes. Built to replace outdated, disconnected systems, Atomx provides ambitious insurance brands with a fully integrated ecosystem that removes silos and delivers the speed, clarity, and control needed to build what’s next. Everything runs on streaming, telemetry, features, and trustworthy data and you will be at the centre of it.

Key Responsibilities Event Drivent Data & ML Telemetry

• Architect and operate the event‑driven telemetry backbone capturing user interactions, system behaviour, ML inference signals, and workflow events across all platforms.
• Define consistent event schemas, taxonomies, and ACORD‑aligned behavioural models to support analytics and ML.

Azure‑Native Data & ML Pipelines

• Build and operate batch and real‑time pipelines using Azure services to ingest, transform, enrich, and validate data across the digital suite.
• Implement ML‑ready data pipelines supporting training, inference, and monitoring workloads.
• Automate data quality checks, lineage, SLAs, and pipeline observability.

Enterprise Feature Store Ownership

• Design, build, and maintain the enterprise Feature Store (Feast, Databricks Feature Store, Hopsworks).
• Ensure feature consistency between training and real‑time inference, with strong governance and discoverability.
• Manage feature versioning, contracts, embeddings, online/offline stores, and historical point‑in‑time correctness.

ML Enablement & Platform Foundations

• Partner closely with ML Engineers and Data Scientists to deliver reusable, production‑grade ML features and transformations.
• Enable historical replay, time‑travel datasets, and backtesting for models across underwriting, pricing, and operational intelligence.
• Support model lifecycle needs including feature monitoring, drift detection inputs, and inference telemetry.

Data Modelling, Governance & Standards

• Implement canonical data and feature schemas aligned with ACORD‑style industry standards and internal capability models.
• Drive end‑to‑end data governance including lineage, metadata cataloguing, schema evolution, and validation frameworks.
• Ensure all ML and analytical data assets are traceable, trusted, and production‑grade.

Cross-Platform Engineering Collaboration

• Work closely with product, platform, and ML teams to ensure consistent real‑time integration patterns and scalable platform design.
• Act as a technical partner in shaping the AI/ML platform roadmap.

Role Requirements

• Strong experience with Python and SQL, plus at least one NoSQL data store.
• Proven experience designing distributed data and ML platforms.
• Solid engineering practices including testing, CI/CD, monitoring, and automated deployments.
• Azure Cloud (Required)
• Hands‑on experience building AI/ML and data platforms on Microsoft Azure.
• Strong knowledge of Azure services such as: Azure Data Factory, Azure Databricks, Azure Event Hub / Kafka, Azure Functions / AKS, Azure Storage (ADLS Gen2). Azure Monitor & Log Analytics.
• Experience securing, scaling, and operating platforms in an enterprise Azure environment.
• Streaming & Feature Platforms
• Practical experience with event streaming (Kafka, Azure Event Hub, Kinesis or equivalent).
• Strong understanding of stream processing concepts: schema evolution, consumer groups, watermarking, and stateful streams.
• Hands‑on experience with Feature Stores and ML data contracts.
• Deep understanding of canonical data modelling, feature lifecycle governance, lineage, and metadata systems.
• Experience supporting ML training, inference, and experimentation workflows.
• Exposure to BI / MI tools such as Power BI or Tableau.

Management Duties* No

We are an equal opportunity employer, and we are proud to share that 93% of our employees say they can be themselves at work. We aim to hire our industry's finest people because the best people drive the best outcomes. And we forever challenge the status quo because we know there are always ways to improve things. Because together, we're limitless.

We value applicants from all backgrounds and foster a culture of inclusivity. We understand the need for flexibility, so work in a hybrid model. Please let us know if you require any reasonable adjustments during the recruitment process.