Slalom Flex (Project Based)- ML Ops Engineer

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. Operating 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. The role focuses on transforming validated machine‑learning models into scalable, governed, and monitored production systems that drive critical business outcomes. You will work within agile, cross‑functional teams alongside data scientists, data engineers, and business stakeholders to deliver reliable and scalable AI solutions.

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.

Requirements

• 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 FastAPI, Flask, or 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, Unity Catalog, Feature Store, and cluster management/optimization.
• Hands‑on experience with MLflow for experiment tracking, model registry, 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, Azure Key Vault, Azure Monitor, and 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

• Base salary pay range: $80–$100 per hour (actual compensation depends on skills, experience, location, and other factors).
• Meaningful time off and paid holidays.
• 401(k) with company match.
• Highly subsidized health, dental, and vision coverage.
• Adoption and fertility assistance.
• Long‑term disability coverage.
• Annual $350 reimbursement account for well‑being‑related expenses.