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Capgemini

Global consulting, technology services, outsourcing

AI Platform Engineer

Applied Machine LearningUpdated on Nov 14
Full-timeAtlanta, GA, USAIn-person

About the Job

Requirements

  • Strong production engineering experience with Python and one systems language; Go or Rust are welcome, along with Kubernetes, infrastructure-as-code, and one major cloud.
  • Hands-on experience with LLM infrastructure, including a model gateway pattern, model serving, and vector or retrieval systems.
  • Experience building with an agent orchestration framework and understanding tool calling and the Model Context Protocol (MCP).
  • Experience building or operating evaluation harnesses, including golden datasets, regression gates in continuous integration, and LLM-judge calibration.
  • Post-training experience through fine-tuning or post-training a model, building its data pipeline, or reproducing techniques from recent papers in production systems.
  • Daily hands-on use of AI coding assistants in the development workflow.

Responsibilities

  • Own one or more platform components end to end, such as the model gateway, agent runtime and orchestration, evaluation harness, guardrail engine, tool and agent registry, or shared product services.
  • Own the training and adaptation loop, including pipelines that turn production traces and evaluation verdicts into reinforcement-learning and fine-tuning datasets, plus infrastructure to train, evaluate, and serve internally tuned LLMs and SLMs behind the same gates as vendor models.
  • Own the reliability, latency, and cost of built systems and maintain platform-service baselines.
  • Provide self-service surfaces for product engineers, with documentation, sensible defaults, and no ticket queue.
  • Co-build with product teams, initially embedding new shared services with a product line and graduating them to platform services when proven.
  • Write specifications, harnesses, evaluations, and guardrails; AI agents execute implementation loops, with engineering judgment focused on review.
  • Apply the three human gates of specification approval, merge, and release; keep regulated code paths involving money movement, authentication, cryptography, and secrets human-owned.

Desired Qualifications

  • Experience with guardrail engines or AI observability, such as NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, or Braintrust.
  • Experience with reinforcement-learning infrastructure, including reward modeling and RLHF- or GRPO-class pipelines, or SLM distillation.
  • Experience with multi-tenant SaaS infrastructure, including tenancy isolation, metering, and usage-based billing.
  • Financial-services engineering experience in banks, insurers, or payment providers; regulated-industry or security-engineering depth is also relevant.
  • Open-source contributions or a portfolio of deployed agents and evaluation suites.
  • Experience with the reference stack or analogous components: self-hosted LangSmith and LangGraph Platform, swappable model-provider gateways, PostgreSQL with pgvector, ClickHouse, S3-compatible object storage, Neo4j Enterprise, agent memory over MCP, MCP-native connectors, OpenTelemetry, Grafana, CNCF-conformant Kubernetes, Helm, and Argo CD; deployment may be to a hyperscaler or on-premises.

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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