Senior MLOps Engineer

Fisher Investments • Tampa, FL

1d • Onsite

About The Position

Fisher Investments is seeking a Senior MLOps Engineer to build the infrastructure, pipelines, and operational practices that bring machine learning and generative AI solutions from experimentation into reliable, secure, and scalable production services. The role involves building, deploying, and operating these solutions in enterprise environments, working with AI leads, ML engineers, and platform teams to turn experimental workflows into production-ready services. The position reports to the AI Team Leader, Technology Innovation.

The engineer will work across Azure and Databricks data and ML pipelines, CI/CD workflows, retrieval-augmented generation (RAG) and context engineering, production APIs and microservices, model-serving services, infrastructure as code, automated quality gates, versioning and experiment tracking, and system monitoring. The role also partners with infrastructure, security, and governance teams on access controls, auditability, and operational safeguards.

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

• 10+ years of experience building data-driven software solutions, including 5+ years of hands-on machine learning engineering experience.
• 8+ years of Python development experience, including building and maintaining production services, APIs, or data pipelines.
• Demonstrated experience deploying and operating machine learning or generative AI solutions in production.
• Experience with Databricks, including Delta Lake, Auto Loader, Workflows, Feature Engineering, and deployment automation using Databricks Asset Bundles.
• Hands-on experience building CI/CD pipelines and implementing automated testing and deployment workflows for data and ML systems.
• Hands-on experience with cloud-native AI infrastructure and related technologies, including Azure Foundry, Azure Kubernetes Service, Docker, and GPU-backed workloads.
• Hands-on experience integrating and operating graph databases to support knowledge graphs, relationship-based retrieval, or generative AI applications.
• Bachelor's degree in computer science, Artificial Intelligence, or a related field, or equivalent professional experience.

Nice To Haves

• Experience deploying models for low-latency, high-throughput inference using frameworks such as vLLM and other GPU-based model-serving tools.
• Experience with distributed training, GPU optimization, and GPU computing technologies such as CUDA and RAPIDS.

Responsibilities

• Design, build, and operate end-to-end data and ML pipelines on Azure and Databricks, including ingestion, validation, feature engineering, model training, fine-tuning, evaluation, and deployment.
• Build and maintain CI/CD workflows for application code, model artifacts, prompt templates, retrieval and context configurations, evaluation datasets, and infrastructure.
• Operationalize RAG and context engineering workflows, including document ingestion, chunking, embedding generation, indexing, retrieval, context selection and assembly, and evaluation of retrieval and response quality.
• Collaborate with ML engineers, data engineers, and the AI Lead to translate experimental training, fine-tuning, evaluation, and inference workflows into reproducible, production-ready systems.
• Develop and maintain production-grade APIs and microservices using Python and FastAPI to expose model inference and integrate AI capabilities into enterprise applications.
• Deploy, operate, and scale model-serving endpoints and containerized inference services using Azure-managed services, Docker, and Kubernetes according to workload requirements.
• Provision and manage cloud and platform resources through infrastructure as code, ensuring consistent, repeatable, and auditable environment configurations.
• Implement automated testing and release quality gates for application code, data quality, model performance, retrieval quality, AI safety, and deployment readiness.
• Establish model and artifact versioning, experiment tracking, and deployment traceability to support reproducibility and troubleshooting.
• Monitor and improve model quality, data and retrieval pipeline reliability, inference latency, throughput, service availability, and infrastructure cost efficiency.
• Partner with infrastructure, security, and governance teams to implement appropriate access controls, auditability, and operational safeguards.

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 back-up child, adult, and elder care.
• Learning and development framework customized for every employee.

What This Job Offers

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