10+ years of experience in data-driven software solutions, with 5+ years in machine learning engineering
8+ years of Python development experience, focusing on production services and APIs
Proven track record of deploying and operating machine learning or generative AI solutions in production
Experience with Databricks, including Delta Lake and deployment automation
Hands-on experience building CI/CD pipelines for data and ML systems
Familiarity with cloud-native AI infrastructure, particularly Azure and Kubernetes
Bachelor's degree in computer science, AI, or related field, or equivalent experience
Responsibilities
Design and operate end-to-end data and ML pipelines on Azure and Databricks
Build and maintain CI/CD workflows for various model artifacts and configurations
Operationalize retrieval-augmented generation workflows for document processing
Collaborate with teams to convert experimental workflows into production-ready systems
Develop production-grade APIs and microservices using Python and FastAPI
Deploy and scale model-serving endpoints using Azure services and Docker
Implement automated testing and quality gates for deployment readiness
Benefits
100% paid medical, dental, and vision premiums for you and qualifying dependents
50% 401(k) match, up to the IRS maximum
20 days of PTO plus 10 paid holidays
Family support programs including paid caregiver leave and fertility assistance
In-office role with potential for hybrid work based on eligibility
Full Job Description
Overview
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 toolsExperience with distributed training, GPU optimization, and GPU computing technologies such as CUDA and RAPIDS
100% paid medical, dental and vision premiums for you and your 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
This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change.
About Fisher Investments
Fisher Investments is a privately owned investment management firm founded in 1979 by Ken Fisher. The company is headquartered in Camas, Washington, and has offices in the United States, Europe, and Asia. Fisher Investments manages assets for individuals and institutions, including pension plans, endowments, and foundations. The company is known for its investment philosophy, which emphasizes a global perspective and a focus on long-term growth. Fisher Investments has been recognized for its performance and has won numerous awards for its investment strategies.
