AI Platform Engineer

Capgemini • Atlanta, GA

1d • $129,000 - $143,000

About The Position

Capgemini is hiring an AI Platform Engineer to build and operate shared platform components for AI products. 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 covers the training and adaptation loop that converts production traces and evaluation verdicts into reinforcement-learning and fine-tuning datasets, and the infrastructure for training, evaluating, and serving tuned LLMs and SLMs behind the same controls as vendor models.

The engineer is accountable for the reliability, latency, and cost of platform services, and for delivering self-service components with documentation and usable defaults. The role involves embedding with product teams to co-build shared services before graduating proven components into the platform. The supported reference stack includes LangSmith and LangGraph Platform, swappable model providers, PostgreSQL with pgvector, ClickHouse, S3-compatible storage, Neo4j Enterprise, MCP, OpenTelemetry, Grafana, Kubernetes, Helm, and Argo CD; comparable technologies and experience are accepted.

The team is small and senior by design. Engineers write specifications, harnesses, evaluations, and guardrails, while AI agents execute implementation loops. Human review is focused on spec approval, merge, and release, with regulated code paths such as money movement, authentication, cryptography, and secrets remaining human-owned.

Requirements

• Strong production engineering experience with Python and one systems language; Go or Rust is welcome.
• Experience 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 of tool calling and the Model Context Protocol (MCP).
• Evaluation engineering experience, including evaluation harnesses, golden datasets, regression gates in CI, and LLM-judge calibration; able to explain why evaluations gate merges.
• Post-training experience 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 workflows.
• Depth in four or five of the listed technical areas is valued more than surface familiarity with all of them.

Nice To Haves

• Guardrail-engine or AI-observability experience, such as NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, or Braintrust.
• Reinforcement-learning infrastructure experience, including reward modelling or RLHF/GRPO-class pipelines, or SLM distillation experience.
• Multi-tenant SaaS infrastructure experience, including tenancy isolation, metering, or usage-based billing.
• Financial services engineering experience with banks, insurers, or payment providers; other regulated-industry or security engineering depth also counts.
• Open-source contributions or a portfolio of deployed agents and evaluation suites.
• Experience with technologies comparable to the reference stack; exact tool-for-tool matching is not expected.

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 pipelines that turn production traces and evaluation verdicts into reinforcement-learning and fine-tuning datasets.
• Build infrastructure to train, evaluate, and serve tuned LLMs and SLMs alongside vendor models.
• Be accountable for the reliability, latency, and cost of platform services.
• Provide self-service components for product engineers with documentation, sensible defaults, and no ticket queue.
• Co-build shared services with product teams, initially embedded with a product line and graduating proven services to the platform.
• Write specifications, harnesses, evaluations, and guardrails, and review AI-agent implementation work.
• Maintain human ownership of regulated code paths such as money movement, authentication, cryptography, and secrets.

Benefits

• Paid time off based on employee grade, including 12–25 vacation days depending on grade, company-paid holidays, personal days, and sick leave.
• Medical, dental, and vision coverage, or provincial healthcare coordination in Canada.
• Retirement savings plans, such as a 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.
• The role may be eligible for variable incentives, bonuses, or commissions, depending on the position and applicable laws.

What This Job Offers

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