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 experience8+ 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…