Slalom Flex (Project Based)- ML Ops Engineer

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

Location: Remote / Hybrid (Client-Facing Consulting Engagement)
Employment Type: Full-Time Consultant
Duration: Project Based Contract Through End of Year with Likely Extension

About Us

Slalom is a purpose-led, global business and technology consulting company. From strategy to implementation, our approach is fiercely human. In six+ countries and 43+ markets, we deeply understand our customers—and their customers—to deliver practical, end-to-end solutions that drive meaningful impact. Backed by close partnerships with over 400 leading technology providers, our 10,000+ strong team helps people and organizations dream bigger, move faster, and build better tomorrows for all. We’re honored to be consistently recognized as a great place to work, including being one of Fortune’s 100 Best Companies to Work For seven years running. Learn more at Slalom.com.

About the Role

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

The ideal candidate is a hands‑on engineer with deep expertise in MLOps, model deployment, Azure Databricks, and cloud‑native machine learning platforms. You will work within agile, cross‑functional teams alongside data scientists, data engineers, and business stakeholders to deliver reliable and scalable AI solutions.

This is an opportunity to work on complex analytics challenges involving forecasting, optimization, decision support, and advanced AI applications at enterprise scale.

What You’ll Do

• 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.

What You’ll Bring

• 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 Azure Data Lake Storage Gen2 (ADLS), Azure Key Vault, Azure Monitor, Cloud cost optimization practices
• Excellent communication, collaboration, and problem‑solving skills.
• Ability to operate independently and lead technical delivery efforts within agile, cross‑functional teams.

Preferred Qualifications

• Experience delivering Generative AI or Large Language Model (LLM) solutions in production environments.
• Experience developing Retrieval Augmented Generation (RAG) applications and AI‑powered decision‑support tools.
• Experience integrating machine learning services with user‑facing applications and analytics platforms.
• Knowledge of model governance, responsible AI practices, and regulated software development environments.
• Experience supporting production workloads requiring high availability, scalability, and observability.

Compensation and Benefits

Slalom prides itself on helping team members thrive in their work and life.

• meaningful time off and paid holidays
• 401(k) with a match
• a range of choices for highly subsidized health, dental, & vision coverage
• adoption and fertility assistance
• long‑term disability
• yearly $350 reimbursement account for any well‑being‑related expenses

Slalom is committed to fair and equitable compensation practices. For this position, the base salary pay range is 80-100/hr. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time.

Slalom is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Slalom will also consider qualified applications with criminal histories, consistent with legal requirements.

Slalom welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process.

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