Remote ML Ops Engineer: Production AI & MLOps Lead

Slalom; Tampa (FL); On-site; USD 110,208 - 137,760; Full time

Benefits offered by this job

401(k) with match

Health insurance

Dental insurance

Vision insurance

Adoption assistance

Fertility assistance

Long-term disability

Well-being reimbursement

Job summary

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade ML and Generative AI solutions in a highly regulated enterprise environment.

The ideal candidate will own end-to-end ML production lifecycle, collaborate with data scientists and engineers, and scale AI across enterprise workloads using Azure Databricks, MLflow, and CI/CD practices in a remote/hybrid consulting setting.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or a related quantitative discipline.
• 5+ years hands-on experience in Machine Learning Engineering, MLOps, or related software engineering roles supporting production AI systems.
• Demonstrated experience deploying and operating machine learning solutions in enterprise environments.
• Strong Python development skills and proficiency with ML frameworks such as Scikit-learn, PyTorch, and TensorFlow.
• Experience developing and deploying APIs and microservices using FastAPI, Flask, MLflow Model Serving.
• Experience deploying and supporting both front-end and back-end applications in Azure environments.
• Deep expertise with Azure Databricks, including Apache Spark, Delta Lake, Databricks, Unity Catalog, Feature Store, Cluster management and optimization.
• Strong hands-on experience with MLflow for Experiment tracking, Model registry, Model packaging, and Automated deployment.
• Experience implementing CI/CD pipelines using Azure DevOps and/or GitHub Actions.
• Strong knowledge of Git-based development workflows, branching strategies, and pull request processes.
• Experience building, packaging, and deploying containerized applications using Docker.
• Working knowledge of Azure cloud services, including ADLS, Key Vault, Monitor, and cost optimization practices.

Responsibilities

• Design, build, and deploy production-grade machine learning and Generative AI solutions that solve complex business challenges.
• Own the end-to-end machine learning production lifecycle, including data ingestion, feature engineering, model deployment, monitoring, and lifecycle management.
• Develop, maintain, and optimize MLOps pipelines using Azure Databricks, MLflow, Unity Catalog, and automated CI/CD processes.
• Implement scalable model-serving architectures, including real-time APIs, batch inference pipelines, and feature stores.
• Convert data science prototypes and experimental notebooks into maintainable, production-ready software solutions.
• Collaborate with data engineering teams to ensure data pipelines meet performance and quality requirements.
• Establish and maintain best practices for model versioning, reproducibility, deployment automation, monitoring, drift detection, A/B testing, and automated retraining.
• Build and manage online and batch model-serving endpoints, compute infrastructure, monitoring frameworks, and performance dashboards.
• Ensure compliance with data governance, privacy, security, and responsible AI standards.
• Communicate technical decisions, architecture patterns, and trade-offs effectively to stakeholders.

Skills

Python

MLOps

Azure Databricks

Model deployment

CI/CD

Docker

Azure

MLflow

API development

Education

Bachelor’s or Master’s in CS/SE/Math/Data Science

Tools

MLflow

FastAPI

Flask

Unity Catalog

GitHub Actions

Azure DevOps

Docker

Job description

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade ML and Generative AI solutions in a highly regulated enterprise environment.

The ideal candidate will own end-to-end ML production lifecycle, collaborate with data scientists and engineers, and scale AI across enterprise workloads using Azure Databricks, MLflow, and CI/CD practices in a remote/hybrid consulting setting.