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About the Role

As an AI Platform & Site Reliability Engineering Managing Consultant, you will help clients design, build and scale secure, reliable and operationally effective AI platforms. You will combine expertise in platform engineering, Site Reliability Engineering (SRE), observability and intelligent operations to help organisations move from isolated AI experimentation to production-grade, enterprise-scale AI services. You will work with technology, engineering, operations and business leaders to establish the platforms, operating models, governance and reliability practices required to run AI-enabled services safely, effectively and at scale. Acting as a trusted advisor to senior stakeholders, you will shape client strategy while leading delivery teams and helping grow our AI Platform & Reliability Engineering capability.

Responsibilities

Assess, define and evolve enterprise AI platform architectures, covering LLM and agentic frameworks, AI gateways, model lifecycle management, data platforms, MLOps/LLMOps foundations and integration patterns.
Support clients in evaluating build, buy and hybrid approaches aligned to business needs, risk appetite and operational requirements.
Design and implement scalable AI platform capabilities including model deployment pipelines, prompt and model management, evaluation frameworks, AI observability, platform automation and operational guardrails.
Enable reliable and repeatable delivery of AI services from experimentation through to production.
Establish SRE practices including SLIs, SLOs, error budgets, capacity planning, resilience engineering and reliability governance.
Help clients shift from reactive operations to data-driven reliability management while balancing reliability, innovation and delivery velocity.
Define observability strategies across applications, platforms and AI workloads using metrics, logs, traces and telemetry.
Establish operational insight models that support proactive decision-making and enable advanced capabilities including anomaly detection, event intelligence, noise reduction and predictive operational analytics.
Apply reliability engineering principles to AI-enabled services, monitoring AI-specific failure modes such as data quality degradation, hallucination patterns, token consumption, agent reliability and model performance drift.
Implement controls, feedback loops and automated guardrails to ensure AI services remain secure, trusted and cost-effective.
Design and embed AI governance frameworks, model risk controls, compliance measures and responsible AI practices that address security, regulatory and ethical requirements while supporting innovation and adoption at scale.
Identify opportunities to reduce operational complexity and toil through engineering-led automation, intelligent workflows and AI-enhanced operational practices.
Help clients improve scalability, consistency and operational performance while reducing manual effort.
Act as a trusted advisor to CIO, CTO, CDO and Engineering leadership stakeholders, shaping platform strategies, operating models, vendor selections and transformation roadmaps.
Lead consulting teams and workstreams from assessment and strategy through implementation and scale-up.

Requirements

Proven experience designing, delivering and operating cloud-native, platform engineering, AI platform or reliability engineering solutions within complex enterprise environments.
Strong understanding of AI platform architectures including LLMOps, MLOps, agentic AI frameworks, model lifecycle management and AI operational controls.
Experience establishing and scaling SRE practices including observability, SLIs, SLOs, error budgets, incident management and reliability engineering.
Strong understanding of AI governance, responsible AI, regulatory requirements and model risk management.
Experience implementing observability strategies using modern monitoring, telemetry and operational analytics platforms.
Demonstrated ability to advise senior stakeholders and lead multidisciplinary transformation programmes.
Experience working across hyperscaler ecosystems including Azure, AWS and Google Cloud Platform.
Proven ability to balance business outcomes, user needs, engineering constraints and operational requirements when shaping platform strategies.
Experience with AI observability, model monitoring, AI governance tooling or AI platform operations.
Experience of platform engineering, DevSecOps, automation and Infrastructure-as-Code practices.
Experience developing propositions, leading bids and supporting business growth activities.
Active participation in AI, SRE, platform engineering or cloud communities.
Must obtain Security Check (SC) clearance.
Must have resided continuously within the United Kingdom for the last 5 years to obtain SC clearance.
Must be fully flexible in terms of assignment location, as these roles may involve periods of time away from home at short notice.

Skills

AI Platform Strategy & Architecture
AI Platform Engineering & LLMOps
Reliability Engineering & SRE
Observability & Operational Intelligence
AI Operations & Service Reliability
Responsible AI & Platform Governance
Automation & Operational Efficiency
Client Advisory & Transformation Leadership
Cloud-native
Platform engineering
AI platform
Reliability engineering
LLMOps
MLOps
Agentic AI frameworks
Model lifecycle management
AI operational controls
Observability
SLIs
SLOs
Error budgets
Incident management
AI governance
Responsible AI
Model risk management
Monitoring
Telemetry
Operational analytics
Azure
AWS
Google Cloud Platform
DevSecOps
Infrastructure-as-Code
Azure AI Engineer Associate
Azure Solutions Architect Expert
AWS Machine Learning Specialty
Google Professional Cloud Architect
Certified Kubernetes Administrator (CKA)
SRE Foundation or SRE Practitioner
Datadog
Dynatrace
Splunk

Location

London
Manchester
Glasgow

Work Type

Hybrid working
Flexible working arrangements

Experience Level

Managing Consultant

Benefits

Flexible benefits options
Variable element dependent on grade and company/personal performance
Opportunities for learning and certification through internal and partner led programmes
Training from industry experts on management consulting and client delivery
Access to Les Fontaines training environment
Monthly showcases of initiatives
Monthly team drinks
Regular leadership connect sessions
Team away days
Mental Health Champions
Wellbeing apps (Thrive and Peppy)

About the Company

Capgemini Invent believes difference drives change.
As inventive transformation consultants, they blend strategic, creative and scientific capabilities, collaborating closely with clients to deliver cutting-edge solutions.
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society.
It is a responsible and diverse group of 340,000 team members in more than 50 countries.
With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs.
It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fuelled by its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
The Group reported 2023 global revenues of €22.5 billion.

Equal Opportunity

Capgemini aims to build an environment where employees can enjoy a positive work-life balance.
Employee wellbeing is vitally important.
Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government’s Disability Confident scheme.
As part of their commitment to inclusive recruitment, they will offer an interview to all candidates who declare they have a disability and meet the minimum essential criteria for the role.