Remote ML Ops Engineer for Production AI Solutions

Slalom Build; United States; Hybrid; USD 110,208 - 137,760; Full time

Benefits offered by this job

Paid time off

401(k) match

Health coverage

Dental coverage

Vision coverage

Adoption assistance

Fertility assistance

Long-term disability

Well-being reimbursement

Job summary

Slalom Build 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 brings hands-on MLOps expertise, model deployment skills, and deep Azure Databricks experience to collaborate with data scientists, data engineers, and business stakeholders in agile teams delivering reliable AI at scale.

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or related quantitative discipline.
• 5+ years of 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: Scikit-learn, PyTorch, 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: 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 PR processes.
• Experience containerizing applications with Docker.
• Working knowledge of Azure services including ADLS Gen2, Key Vault, etc.

Responsibilities

• Design, build, and deploy production‑grade ML and Generative AI solutions that solve complex business challenges.
• Own the end‑to‑end ML production lifecycle: 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 and batch inference pipelines.
• Convert data science prototypes into production‑ready software solutions.
• Collaborate with data engineering to ensure data pipelines meet performance and quality requirements.
• Establish best practices for model versioning, deployment automation, monitoring, drift detection, A/B testing, and retraining.
• Build and manage online and batch model‑serving endpoints, compute infra, monitoring, and dashboards.
• Ensure compliance with data governance, privacy, security, and responsible AI standards.
• Communicate architectural decisions and trade‑offs to stakeholders.

Skills

MLOps

Python

Model deployment

Azure Databricks

Scikit-learn

PyTorch

TensorFlow

FastAPI/Flask

CI/CD

Azure Cloud

Education

Bachelor's or Master's degree in CS/SE/Math/Stats/Data Science

Tools

MLflow

Unity Catalog

Delta Lake

Docker

GitHub Actions

Azure DevOps

MLflow Model Serving

Job description

Slalom Build 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 brings hands-on MLOps expertise, model deployment skills, and deep Azure Databricks experience to collaborate with data scientists, data engineers, and business stakeholders in agile teams delivering reliable AI at scale.