Remote ML Ops Engineer - Build Production AI Pipelines

Slalom; City of White Plains (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 AI/ML Engineer to design, deploy, and operate production‑grade machine learning and Generative AI solutions in a regulated enterprise environment. You will transform validated models into scalable, governed systems partnering with data scientists and engineers in a cross‑functional, agile setting.

The role emphasizes end‑to‑end ML lifecycle, MLOps, Azure Databricks, and cloud‑native platforms to deliver reliable AI solutions across forecasting and decision support use cases.

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, cost optimization practices
• Excellent communication, collaboration, and problem‑solving skills.
• Ability to operate independently and lead technical delivery efforts within agile, cross‑functional teams.

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

ML Engineering

MLOps

Azure Databricks

Docker

CI/CD

API Development

Git/GitHub Actions

Education

Bachelor's or Master’s in CS/SE/Math

Tools

MLflow

Azure Databricks

Delta Lake

Unity Catalog

Spark

MLflow Model Serving

Job description

Slalom is seeking an AI/ML Engineer to design, deploy, and operate production‑grade machine learning and Generative AI solutions in a regulated enterprise environment. You will transform validated models into scalable, governed systems partnering with data scientists and engineers in a cross‑functional, agile setting.

The role emphasizes end‑to‑end ML lifecycle, MLOps, Azure Databricks, and cloud‑native platforms to deliver reliable AI solutions across forecasting and decision support use cases.