AI Engineer

Fisher Investments • Tampa, FL

1d • Onsite

About The Position

Fisher Investments is expanding its generative AI capabilities to support international growth. The AI Engineer will design, build, and improve advanced AI applications using large language models, GPU-accelerated computing, and modern API frameworks. The role involves deploying AI applications into scalable production environments and integrating them into enterprise applications using secure, cloud-native architectures.

The engineer will work with the AI Infrastructure team on LLM pipelines, including training workflows and retrieval-augmented generation (RAG) systems. The position also supports data ingestion and orchestration pipelines, graph-enhanced retrieval solutions, and the reliability, explainability, performance, and regulatory compliance of AI applications. This is an in-office role; depending on role, tenure, and performance eligibility, the employee may have the opportunity to participate in a hybrid work-from-home program, which is subject to change.

Requirements

• At least 6 years of experience building data-driven software solutions.
• At least 3 years of hands-on AI development experience.
• Proficiency in Python and practical experience building applications with LLMs, embeddings, vector databases, and RAG architecture.
• Demonstrated experience with generative AI models, including multimodal models.
• Hands-on experience developing and deploying AI applications using Azure Foundry, Docker, Kubernetes, and GPU-backed compute.
• Bachelor's degree in computer science, Artificial Intelligence, or a related field, or equivalent professional experience.
• Experience fine-tuning, evaluating, and deploying models using Azure-managed AI services and compute resources.
• Familiarity with agent frameworks and experience deploying AI agents with tool integration, appropriate permissions, and operational safeguards.
• Experience building graph-enhanced RAG and hybrid retrieval solutions using graph databases such as Neo4j, Stardog, or Azure Cosmos DB for Apache Gremlin.
• Experience deploying models with low latency and high throughput using frameworks such as vLLM and other GPU model deployment tools.
• Hands-on experience building CI/CD pipelines and automated testing workflows for generative AI applications.
• Experience with distributed training, GPU optimization, and large-scale model frameworks such as CUDA and RAPIDS.

Responsibilities

• Design, develop, fine-tune, and deploy generative AI models in scalable production environments.
• Build and maintain FastAPI APIs and microservices that expose AI capabilities across the enterprise.
• Collaborate with the AI Infrastructure team to architect LLM pipelines, including training workflows and RAG systems.
• Integrate AI solutions into enterprise applications using secure, cloud-native architectures and best practices.
• Build and operate data ingestion, transformation, and orchestration pipelines using Databricks, Delta Lake, Auto Loader, and Workflows.
• Integrate graph databases into AI applications to model relationships and support graph-enhanced RAG and hybrid retrieval.
• Ensure AI models are explainable, reliable, and compliant with regulatory and internal governance standards.
• Monitor and improve application and model performance using evaluation frameworks, observability tools, and iterative changes to prompts, retrieval, context, and model configuration.

Benefits

• 100% paid medical, dental, and vision premiums for the employee and qualifying dependents.
• A 50% 401(k) match, up to the IRS maximum.
• 20 days of PTO, plus 10 paid holidays.
• Family Support programs, including 8-week Paid Primary Caregiver Leave, $10,000 fertility, family-forming, and hormonal health assistance, and backup child, adult, and elder care.
• Potential eligibility to participate in a hybrid work-from-home program, depending on role, tenure, and performance; the program is subject to change.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior