Senior ML Ops Engineer — Remote AI Deployment

Slalom

Columbia (SC)

Hybrid

USD 110,000 - 138,000

Full time

3 days ago

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Benefits offered by this job

401(k) with match

Health, dental, & vision coverage

Adoption and fertility assistance

Long-term disability

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. You will transform validated models into scalable, governed systems and collaborate with data scientists, engineers, and business stakeholders.

The role emphasizes hands-on MLOps, model deployment, Azure Databricks, and cloud-native platforms within an agile, client-facing team.

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 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.

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.