Job Description
A growing fintech company focused on modern financial services and trading technology is looking for an AI Infrastructure Engineer to build and operate the platform supporting its next generation of AI-powered financial workflows. The company combines financial services expertise with advanced data, AI, and engineering capabilities, with a strong focus on security, auditability, correctness, and reliability. The engineering environment is primarily built around the Microsoft Azure ecosystem and integrates closely with .NET-based internal systems and sensitive financial data.
As an AI Infrastructure Engineer, you'll own the platform layer that turns advanced AI models into reliable, secure, and production-ready internal services. You'll design and operate infrastructure for both large-scale and specialized models, build secure APIs for AI-powered applications, manage GPU-based workloads across development and production, and integrate model serving with retrieval systems, databases, internal services, and authentication. This is a hands-on platform engineering role where you'll work closely with AI engineers to support fine-tuning, evaluation, and deployment while establishing standards for model packaging, versioning, rollout, and rollback.
You'll also play a key role in building the engineering infrastructure that allows AI systems to operate reliably at scale. This includes developing CI/CD pipelines, implementing observability across latency, throughput, errors, GPU utilization, and service health, and supporting batch, interactive, and evaluation inference workloads. The ideal candidate has a strong infrastructure or platform engineering background, is comfortable working across applications and underlying infrastructure, and enjoys solving complex problems involving performance, reliability, security, and cost optimization.
This is a full-time, hybrid position based in Miami, FL, with flexibility for partial work from home. You'll have the opportunity to work at the intersection of AI, cloud infrastructure, and financial technology while helping establish the platform standards and systems that will support the company's growing AI capabilities.
Required Skills & Experience
- 5+ years of experience in infrastructure, platform engineering, DevOps, SRE, or ML platform engineering
- Strong experience with Microsoft Azure
- Strong experience with Kubernetes, preferably Azure Kubernetes Service (AKS)
- Hands-on experience with Docker and containerized services
- Strong C# / .NET experience, particularly for internal service integration
- Good Python skills for automation, AI infrastructure, and scripting
- Experience building production APIs and internal developer platforms
- Experience with CI/CD pipelines, infrastructure as code, and observability
- Strong Linux skills
- Experience operating systems with high security and reliability requirements
- Ability to debug complex issues across applications, infrastructure, networking, and storage
Desired Skills & Experience
- Experience working with GPU clusters or distributed compute environments
- Experience serving large language models or other deep learning models in production
- Experience with high-throughput batch processing
- Familiarity with model registries, MLflow, or Azure Machine Learning
- Experience with financial services infrastructure or secure internal platforms in regulated environments
- Experience with retrieval-augmented generation systems, vector databases, or search infrastructure
- Experience optimizing latency-sensitive services and workloads
What You Will Be Doing Daily Responsibilities
- Design and operate infrastructure for large-scale and specialized AI models
- Build secure internal APIs supporting AI-powered applications
- Deploy and manage GPU-based workloads across development and production environments
- Integrate model-serving infrastructure with retrieval systems, databases, internal services, and authentication
- Build and maintain CI/CD pipelines for AI model and application deployments
- Implement observability across latency, throughput, errors, GPU utilization, and overall service health
- Support batch, interactive, and evaluation inference workloads
- Partner with AI engineers on model fine-tuning, evaluation, and production deployment
- Implement secure access controls, audit logging, and environment separation
- Optimize infrastructure for reliability, cost, and performance
- Define standards for model packaging, versioning, rollout, and rollback
- Troubleshoot complex issues across applications, infrastructure, networking, and storage
The Offer
You will receive the following benefits:
- Competitive Salary
- Hybrid Work Environment
- Opportunity to work at the intersection of AI, cloud infrastructure, and financial technology
- Hands-on ownership of production AI infrastructure
- Opportunity to build foundational systems and engineering standards
- Collaborative environment working closely with AI and engineering teams
Applicants must be authorized to work in the US on a full-time basis now and in the future.
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