Job description

AI/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. 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. As a result, Slalom is proud to invest in benefits that include 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, and long-term disability. We also offer 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. EEO and Accommodations 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. Locations: Albany, NY; Atlanta, GA; Austin, TX; Birmingham, AL; Boston, MA; Buffalo, NY; Charlotte, NC; Chicago, IL; Columbia, SC; Dallas, TX; Detroit, MI; Grand Rapids, MI; Harrisburg, PA; Houston, TX; Miami, FL; Milwaukee, WI; Minneapolis, MN; New Brunswick, NJ; New York, NY; Oklahoma City, OK; Philadelphia, PA; Pittsburgh, PA; Portland, ME; Providence, RI; Raleigh, NC; Richmond, VA; Rochester, NY; San Antonio, TX; Tampa, FL; Washington, DC; White Plains, NY

Requirements

Must-haves

AI/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
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.

Nice-to-haves

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.

Application prep

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Keywords to mirror

aimlmachine learningazureagileci/cdpythondockerllm

Platform: slalom
Effort: medium

Benefits

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Dental

Vision

401(K)