Remote ML Ops Engineer — Project-Based Enterprise AI

Slalom; Harrisburg (Dauphin County); Hybrid; USD 110,208 - 137,760; Full time

Benefits offered by this job

401(k) with match

Health, dental, and vision coverage

Adoption and 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 regulated enterprise environment. You will transform validated models into scalable, governed systems and collaborate with data scientists and data engineers to deliver reliable AI capabilities.

The role emphasizes hands-on MLOps, model deployment, and end-to-end lifecycle management using Azure Databricks, MLflow, and Unity Catalog in a client-facing consulting

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or a 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 machine learning frameworks such as Scikit-learn, PyTorch, and TensorFlow.
• Experience developing and deploying APIs and microservices using frameworks such as 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, streaming architectures, and feature management platforms 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 both technical and non-technical stakeholders.

Skills

Python

Machine Learning

MLOps

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Azure Databricks

MLflow

Unity Catalog

Feature Store

Docker

FastAPI

Flask

Job description

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade ML and Generative AI solutions in a regulated enterprise environment. You will transform validated models into scalable, governed systems and collaborate with data scientists and data engineers to deliver reliable AI capabilities.

The role emphasizes hands-on MLOps, model deployment, and end-to-end lifecycle management using Azure Databricks, MLflow, and Unity Catalog in a client-facing consulting