AI Platform Engineer

GlobalLogic Inc.; Greater London; On-site; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Private medical insurance

Pension scheme

Annual volunteering days

Training budget

Health Cash Plan

Employee recognition program

Wellbeing initiatives

Job summary

GlobalLogic Inc. is seeking an experienced Contract Technical Lead / Lead AI Platform Engineer to own end-to-end delivery of the AI Control Tower platform. You will steer architecture, collaborate with SMEs, and ensure secure, observable, scalable AI systems.

You will shape the technical roadmap, set engineering standards, and drive robust data pipelines and monitoring. This 12-month contract reinforces governance, resilience, and responsible AI practices.

Qualifications

• Strong engineering foundations with experience building scalable distributed systems or data platforms.
• Fluency in Python, SQL, Java and modern data processing frameworks.
• Expertise in cloud-based AI/ML ecosystems, particularly AWS SageMaker.
• Proven experience developing monitoring frameworks, observability pipelines, and dashboards.
• Deep understanding of event-driven architectures and messaging systems (Kafka, Vert.x, or similar).
• Knowledge of security engineering, IAM principles, encryption, and cloud security controls. Experience with CI/CD, infrastructure-as-code, and automated testing for data/ML systems.
• Exposure to MLOps, LLMOps, or model lifecycle management.
• Awareness of model risk and regulatory frameworks (e.g., SS1/23, NIST AI RMF).
• Understanding of operational resilience concepts and SRE practices (SLIs/SLOs).
• Experience with data lineage or governance tooling (DataHub, Glue, Collibra).
• Interest in Responsible AI, explainability, fairness/bias, and governance automation.

Responsibilities

• Lead the architecture, design, and engineering of the AI Control Tower platform.
• Build the core framework that enables AI observability, guardrails, performance monitoring, and lifecycle management.
• Shape the technical roadmap in partnership with product leaders and ensure delivery against milestones.
• Establish engineering standards, patterns, and integration approaches used across the platform.
• Engineer a robust, resilient and secure AI environment.
• Develop scalable, high-throughput data pipelines and monitoring systems.
• Ensure end-to-end observability across model development, deployment and runtime operations.
• Embed secure-by-design principles and regulatory compliance into the platform architecture.
• Drive technical collaboration with SMEs across data, platform engineering, security, risk, and MLOps.

Skills

Distributed systems

Python

SQL

Java

Cloud AI/ML

SageMaker

Observability

Event-driven architecture

Security engineering

CI/CD

IaC

MLOps/LLMOps

Regulatory awareness

SRE/SLI/SLOs

Data governance tooling

Tools

AWS SageMaker

Kafka

Vert.x

DataHub

Glue

Collibra

CI/CD tools

Job description

We are seeking to hire an experienced Contract Technical Lead / Lead AI Platform Engineer (Contract) into our Engineering Practice. This is a 12 month assignment (inside IR35), to start 2-4 weeks. This is a hands on, high impact role at the intersection of AI governance, distributed systems, observability, and platform engineering to lead technical delivery for an AI centralised platform - Control Tower. We're looking for a Technical Lead to drive the end to end build of this platform, shape its architecture, and partner with deep technical SMEs across the business to ensure AI systems operate with the highest levels of performance, security, and responsible oversight. What We're Looking For You'll bring a blend of strong engineering foundations, platform ownership experience, and an enthusiasm for safe and scalable AI. Specifically:

• Strong engineering foundations, with experience building scalable distributed systems or data platforms.
• Fluency in Python, SQL, Java, and modern data processing frameworks.
• Expertise in cloud-based AI/ML ecosystems, particularly AWS SageMaker (required).
• Proven experience developing monitoring frameworks, observability pipelines, and dashboards.
• Deep understanding of event-driven architectures and messaging systems (Kafka, Vert.x, or similar).
• Knowledge of security engineering, IAM principles, encryption, and cloud security controls. Experience with CI/CD, infrastructure-as-code, and automated testing for data/ML systems.
• Exposure to MLOps, LLMOps, or model lifecycle management.
• Awareness of model risk and regulatory frameworks (e.g., SS1/23, NIST AI Risk Management Framework).
• Understanding of operational resilience concepts and SRE practices (SLIs/SLOs).
• Experience with data lineage or governance tooling (DataHub, Glue, Collibra).
• Interest in Responsible AI, explainability, fairness/bias, and governance automation.
• Lead the architecture, design, and engineering of the AI Control Tower platform.
• Build the core framework that enables AI observability, guardrails, performance monitoring, and lifecycle management.
• Shape the technical roadmap in partnership with product leaders, ensuring delivery against ambitious milestones.
• Establish engineering standards, patterns, and integration approaches used across the platform.
• Engineer a Robust, Resilient, and Secure AI Environment.
• Develop scalable, high throughput data pipelines and monitoring systems.
• Ensure end to end observability across model development, deployment, and runtime operations.
• Embed secure-by-design principles, strong IAM practices, and regulatory compliance into the platform architecture.
• Ensure all AI systems are observable, monitored, and measurable from model training through to production.
• Drive Deep Technical Collaboration and partner with SMEs across data engineering, platform engineering, security, risk, and MLOps and Independent Model Monitoring (IMM).
• Support governance of AI systems through transparent metrics, automated reporting, and integrated control mechanisms and bring thought leadership on Responsible AI, emerging standards, and new tooling.
• Competitive pension scheme to help later on in life and private medical insurance & a Health Cash Plan to call upon when needed.
• Annual Volunteering Days to work with a charity of your choice as we appreciate you may wish to give something back to the community.
• Reward & Recognition -Reward Gateway access to acknowledge when you're doing a great job.
• Holidays - Not just 25 days plus public holidays, you can also buy up to an extra five days to make sure you address that much needed work-life balance.
• Family First - Enhanced family friendly policies and access to on-line shopping discounts platform as we appreciate the other roles you may have in life.
• Health and Wellbeing - From free massages and annual health checks to subsidised gym membership and cycle to work schemes. Frequent Mental Health awareness days and access to a 24/7 EAP, as your welfare is important to us.
• Personal Development - Your personal development is key to us and we make sure you get time to invest in your own personal learning and career progression supported by our generous training budget.
• The Extras - Great coffee, fruit and snacks with beer and wine in the fridge and unforgettable social events, BBQ's and Christmas parties.
  You'll bring a blend of strong engineering foundations, platform ownership experience, and an enthusiasm for safe and scalable AI. Specifically:
• Strong engineering foundations, with experience building scalable distributed systems or data platforms.
• Fluency in Python, SQL, Java, and modern data processing frameworks.
• Expertise in cloud-based AI/ML ecosystems, particularly AWS SageMaker (required).
• Proven experience developing monitoring frameworks, observability pipelines, and dashboards.
• Deep understanding of event-driven architectures and messaging systems (Kafka, Vert.x, or similar).
• Knowledge of security engineering, IAM principles, encryption, and cloud security controls. Experience with CI/CD, infrastructure-as-code, and automated testing for data/ML systems., Exposure to MLOps, LLMOps, or model lifecycle management.
• Awareness of model risk and regulatory frameworks (e.g., SS1/23, NIST AI Risk Management Framework).
• Understanding of operational resilience concepts and SRE practices (SLIs/SLOs).
• Experience with data lineage or governance tooling (DataHub, Glue, Collibra).
• Interest in Responsible AI, explainability, fairness/bias, and governance automation.