Senior MLOps Engineer

It is an exciting time to join Fisher Investments as we expand our Machine Learning capabilities to support our international growth. We are looking for 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 Opportunity:

As a Senior MLOps Engineer, you will build, deploy, and operate machine learning and generative AI solutions in enterprise environments. Working closely with AI leads, ML engineers, and platform teams, you will turn experimental workflows into production-ready services and establish the operational practices needed to support them. You will report to the AI Team Leader, Technology Innovation.

The Day-to-Day:

• 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 retrieval-augmented generation (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 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

Your

Qualifications:

• 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
• 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

Why Fisher Investments:

We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like:

• 100% paid medical, dental and vision premiums for you and your qualifying…