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Capgemini

Global consulting, technology services, outsourcing

AI Platform & Site Reliability Engineering Managing Consultant

Platform Engineering +1Updated on Nov 15
Full-timeNo salary listedHybrid

About the Job

Requirements

  • Proven experience designing, delivering, and operating cloud-native, platform engineering, AI platform, or reliability engineering solutions in 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, service-level indicators, service-level objectives, 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.
  • To be appointed, the successful applicant must obtain Security Check (SC) clearance.
  • To obtain SC clearance, the successful applicant must have resided continuously within the United Kingdom for the last five years and meet other criteria and requirements.

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 production.
  • Establish SRE practices, including service-level indicators, service-level objectives, 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 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.
  • Contribute to business development by leading or contributing to proposals, RFPs, bids, proposition development, client pitches, and client hosting at events.
  • Contribute internally through campaign development, internal think-tanks, whitepapers, practice development, recruitment, team events and activities, and offering development.
  • Maintain flexibility in assignment location; assignments may involve periods away from home at short notice.

Desired Qualifications

  • Experience with AI observability, model monitoring, AI governance tooling, or AI platform operations.
  • Experience in 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.
  • Desirable certifications include Azure AI Engineer Associate, Azure Solutions Architect Expert, AWS Machine Learning Specialty, Google Professional Cloud Architect, Certified Kubernetes Administrator, SRE Foundation or SRE Practitioner, and relevant observability platform certifications such as Datadog, Dynatrace, or Splunk.

About the Company

Capgemini

Global consulting, technology services, outsourcing

Capgemini provides consulting, technology services, and outsourcing to help organizations transform digitally. Its offerings include strategy and transformation consulting, system integration, application development, IT infrastructure management, and managed IT operations. It differentiates itself with a global presence in over 50 countries and a focus on applying AI, cloud, and cybersecurity to real business problems. Its goal is to help clients navigate digital transformation and improve efficiency and innovation through tailored, contract-based or managed services.

Company size
10,001+
Company stage
IPO
Headquarters
Paris, France
Founded
1967

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