AI Platform Engineer

Capgemini • Atlanta, GA

2d • $110,000 - $139,992

About The Position

Capgemini is hiring an AI Platform Engineer to build and operate platform components for AI products and services. The role may own components such as a model gateway, agent runtime and orchestration, evaluation harness, guardrail engine, tool and agent registry, or shared product services. It also involves developing pipelines that turn production traces and evaluation verdicts into reinforcement-learning and fine-tuning datasets, and infrastructure for training, evaluating, and serving company-tuned LLMs and SLMs alongside vendor models.

The engineer is accountable for the reliability, latency, and cost of platform services and builds self-service components with documentation and sensible defaults. The role works alongside product teams to develop shared services, initially embedded with a product line and transitioned to platform services once proven.

The reference stack includes self-hosted LangSmith and LangGraph Platform, model providers accessed through a swappable gateway, PostgreSQL with pgvector, ClickHouse, S3-compatible object storage, Neo4j Enterprise, an agent memory plane over MCP, MCP-native connectors, OpenTelemetry and Grafana, and CNCF-conformant Kubernetes with Helm and Argo CD. Deployment may be to a hyperscaler or on-premises. Comparable technologies and experience are acceptable.

Engineers write specifications, harnesses, evaluations, and guardrails, while AI agents execute implementation loops. Human approval gates apply to specifications, merges, and releases; regulated code paths involving money movement, authentication, cryptography, and secrets remain human-owned. The posted base compensation range is $110,000–$139,992. The role may also be eligible for variable incentives, bonuses, or commissions. Capgemini offers regular full-time employees a benefits package subject to local policy and eligibility.

Requirements

• Strong production engineering experience with Python and one systems language; Go or Rust is welcome.
• Experience with Kubernetes, infrastructure as code, and at least one major cloud.
• Hands-on LLM infrastructure experience, 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 CI, and LLM-judge calibration; ability to explain why evaluations gate merges.
• Post-training fluency demonstrated through fine-tuning or post-training a model, building its data pipeline, or reproducing recent research techniques in production systems.
• Daily hands-on use of AI coding assistants in personal development workflow.
• Depth in four or five of the listed technical areas is valued more than surface familiarity with all areas.

Nice To Haves

• Guardrail-engine or AI-observability experience, such as NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust, or equivalent.
• Reinforcement-learning infrastructure experience, including reward modeling or RLHF- or GRPO-class pipelines, or SLM distillation experience.
• Multi-tenant SaaS infrastructure experience, including tenancy isolation, metering, or usage-based billing.
• Financial services engineering experience in banking, insurance, or payments; experience in another regulated industry or security engineering also counts.
• Open-source contributions or a portfolio of deployed agents and evaluation suites.
• Experience with systems comparable to the reference stack; a tool-for-tool match is not required.

Responsibilities

• Own one or more AI platform components end to end, such as a model gateway, agent runtime and orchestration, evaluation harness, guardrail engine, tool and agent registry, or shared product services.
• Build training and adaptation pipelines that convert production traces and evaluation verdicts into reinforcement-learning and fine-tuning datasets.
• Build infrastructure to train, evaluate, and serve tuned LLMs and SLMs behind the same gates as vendor models.
• Maintain the reliability, latency, and cost baselines of platform services.
• Deliver self-service components for product engineers with documentation, sensible defaults, and no ticket queue.
• Co-build shared services with product teams, initially embedding with a product line and graduating proven services to the platform.
• Write specifications, harnesses, evaluations, and guardrails; review AI-agent implementation work and exercise engineering judgment at specification, merge, and release gates.
• Keep regulated code paths involving money movement, authentication, cryptography, and secrets under human ownership.

Benefits

• Paid time off, with vacation of 12–25 days depending on employee grade, company-paid holidays, personal days, and sick leave, according to policy.
• Medical, dental, and vision coverage, or provincial healthcare coordination in Canada.
• Retirement savings plans, such as 401(k) in the U.S. or RRSP in Canada.
• Life and disability insurance.
• Employee assistance programs.
• Other benefits as provided by local policy and eligibility.
• Possible variable incentives, bonuses, or commissions, depending on position and applicable laws.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed