Remote MLOps Engineer for Enterprise AI Solutions (Contract)

Slalom; Buffalo (NY); Hybrid; USD 110,208 - 137,760; Full time

Benefits offered by this job

401(k) match

Health, dental, & vision coverage

Well-being reimbursement

Job summary

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade machine learning and Generative AI solutions in a regulated enterprise environment. This role focuses on turning validated models into scalable, governed production systems that drive critical business outcomes.

You will work hands-on with MLOps, Azure Databricks, and cloud-native platforms, collaborating with data scientists and engineers in agile teams to deliver reliable AI solutions across

Qualifications

• Bachelor’s or Master’s degree in a quantitative field.
• 5+ years of hands-on experience in Machine Learning Engineering / MLOps.
• Experience deploying and operating machine learning solutions in enterprise environments.
• Strong Python development skills and proficiency with 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 Gen2, Azure Key Vault, Azure Monitor, and cost optimization.

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 notebooks into 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 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

Scikit-learn

PyTorch

TensorFlow

FastAPI

Flask

MLflow

Azure Databricks

Apache Spark

Delta Lake

Unity Catalog

Feature Store

Docker

CI/CD

Git

APIs

Azure

Education

Bachelor’s or Master’s degree in Computer Science / Software Engineering / Mathematics / Statistics / Data Science

Tools

Azure Databricks

MLflow

Docker

Azure DevOps

GitHub Actions

Databricks

Azure

Job description

Slalom is seeking an experienced AI/ML Engineer to design, deploy, and operate production-grade machine learning and Generative AI solutions in a regulated enterprise environment. This role focuses on turning validated models into scalable, governed production systems that drive critical business outcomes.

You will work hands-on with MLOps, Azure Databricks, and cloud-native platforms, collaborating with data scientists and engineers in agile teams to deliver reliable AI solutions across