AI Platform Engineer

Capgemini • Atlanta, GA

3d • $110,000 - $139,992

About The Position

The AI Platform Engineer owns 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. The role also builds the training and adaptation loop that turns production traces and evaluation results into reinforcement-learning and fine-tuning datasets, and provides infrastructure to train, evaluate, and serve tuned large and small language models alongside vendor models.

The engineer is accountable for the reliability, latency, and cost of the platform services they build. They create documented, self-service components with sensible defaults, and work embedded with product teams to prove new shared services before graduating them to the platform. The reference environment includes agent orchestration and evaluation, model-provider gateways, data and knowledge-graph systems, agent memory and MCP-native connectors, observability, and Kubernetes-based deployments. Equivalent experience with comparable technologies is accepted.

The team’s workflow centers on engineers writing specifications, harnesses, evaluations, and guardrails while AI agents execute implementation loops. Human approval is required at specification, merge, and release gates; regulated code paths such as money movement, authentication, cryptography, and secrets remain human-owned.

Requirements

• Strong production engineering experience with Python and one systems language; Go or Rust are welcomed.
• Experience with Kubernetes, infrastructure-as-code, and at least 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, CI regression gates, and LLM-judge calibration, and the ability to explain why evaluations gate merges.
• Post-training experience, such as 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 preferred over surface familiarity with all of them.

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 modelling or RLHF/GRPO-class pipelines, or SLM distillation experience.
• Multi-tenant SaaS infrastructure experience, including tenancy isolation, metering, and usage-based billing.
• Financial services engineering experience with banks, insurers, or payment providers; 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, including agent orchestration and evaluation platforms, model gateways and serving, vector or graph data systems, agent memory, observability, and Kubernetes deployment tooling.

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 behind the same gates as vendor models.
• Ensure the reliability, latency, and cost of owned platform services.
• Provide self-service platform components with documentation and sensible defaults, avoiding ticket-based access.
• 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 participate in human approval gates for specifications, merges, and releases.
• Keep regulated code paths, including money movement, authentication, cryptography, and secrets, under human ownership.

Benefits

• Paid time off, including vacation of 12–25 days depending on employee 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 provided according to local policy and eligibility.

What This Job Offers

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