Capgemini · 3 weeks ago
AI Engineer
United States
Full-time
Remote
Senior Level
$35.60/hr - $55.62/hr
5+ years expCapgemini is a leading provider of consulting and technology services, and they are seeking an AI Engineer to launch and implement GenAI solutions. The role involves developing AI systems for production environments, optimizing performance, and collaborating with production teams to improve operational efficiency.
Artificial Intelligence (AI)ConsultingInformation TechnologyProperty & Casualty InsuranceCloud ComputingSoftwareSustainabilityInsurTechIT Management
Responsibilities
Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access
Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations
Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability
Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes
Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk
Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load
Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness
Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns
Qualification
Required
5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred
3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows
Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine-tuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution)
Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude)
Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions
Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact
Reject if missing: 5+ years of software development experience
Reject if missing: Strong hands-on experience with Python-based application development
Reject if missing: 3+ years building and operating production ML systems
Reject if missing: Practical LLM application development experience
Reject if missing: Experience building RAG pipelines and tool-calling/agentic AI solutions
Reject if missing: Understanding of commercial and open-source LLM ecosystems
Reject if missing: AI safety, governance, or production reliability experience
Reject if unable to design, deploy, and support production-grade AI systems
Preferred
Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation)
Benefits
Medical, dental, vision and retirement benefits
Company
Capgemini
Capgemini is a software company that provides consulting, technology, and digital transformation services.
10001+ employees
H1B Sponsorship
Capgemini has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
48%
Represents job field similar to this job
Engineering and Development
Management and Executive
Sales
Customer Service and Support
Accounting and Finance
Creatives and Design
Marketing
Product Management
Human Resources
Legal and Compliance
Trends of Total Sponsorships
*2026 (1042)
2025 (2788)
2024 (2969)
2023 (3415)
2022 (4386)
2021 (3311)
2020 (5871)
Funding
Current Stage
Public CompanyTotal Funding
$5.66B2026-05-06Post Ipo Debt· $939.87M
2025-09-18Post Ipo Debt· $4.72B
1999-04-01IPO
Leadership Team
Aiman Ezzat
CEO, Capgemini Group
Anirban Bose
CEO of Americas SBU | Member of the Group Executive Board
Recent News
2026-08-15
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2026-08-14
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