AI Commercial & ML Ops Engineer

J  o  b    D  e  s  c  r  i  p  t  i  o  n

Job Profile Title:

AI Commercial & ML Ops Engineer

Job Code:

12824

Business_Title

AI Commercial & ML Ops Engineer

Profile Title:

12824 AI Commercial & ML Ops Engineer

Grade / Band:

IC5

FLSA Status:

Exempt

The AI Commercial & ML Ops Engineer is a senior technical contributor responsible for designing, implementing, and optimizing machine learning and AI model deployment pipelines that support Data Science and Analytics teams. This role ensures models are production-ready, performant, scalable, and maintainable across cloud platforms. The AI & ML Ops Engineer builds and maintains systems that deploy, monitor, and manage machine learning models in production, automating the entire ML lifecycle for scalability, reliability, and efficiency. This role combines hands-on engineering with strategic oversight to establish best practices for ML/AI operations and ensure models deliver real-world business value.

Design, build, and operate end-to-end ML/AI pipelines supporting model training, validation, testing, and deployment across batch, streaming, and real-time inference use cases.

Automate the full ML lifecycle, including data ingestion, feature generation, training, evaluation, deployment, monitoring, and retraining, using reproducible, version-controlled workflows.

Implement CI/CD pipelines for ML and AI systems, enabling automated testing, validation gates, and safe promotion of models across development, staging, and production environments.

Develop and maintain orchestration and lifecycle management workflows using platforms such as Airflow, Kubeflow, and MLflow for pipeline scheduling, experiment tracking, and model governance.

Optimize ML and AI workloads for performance, scalability, and cost efficiency, leveraging distributed compute, caching strategies, workload isolation, and cloud-native services.

Establish robust monitoring, observability, and alerting frameworks for deployed models, including performance metrics, data quality checks, drift detection (data, concept, and prediction), and bias monitoring where applicable.

Design and implement automated retraining strategies, including trigger-based, schedule-based, and performance-based model refresh mechanisms to ensure sustained model effectiveness.

Design repeatable prompting frameworks and best practices for data engineering and data science teams, ensuring consistency, safety, and effectiveness in AI-assisted development workflows.

Implement guardrails for AI-generated code and workflows, including access controls, secrets management, compliance enforcement, and security best practices across ML and AI platforms.

Evaluate, benchmark, and operationalize emerging AI technologies and vendor offerings, identifying high-value use cases, quantifying business impact, and providing technical leadership on adoption decisions.

Partner closely with Data Science, Data Engineering, platform teams, and vendor specialists to translate research and business requirements into scalable, production-ready ML & AI solutions.

Mentor and coach engineers and data scientists on MLOps and AI operational best practices, influencing enterprise-wide standards, architectural patterns, governance, and reliability practices.

Performs other job-related duties as requested

Proof of eligibility to work in the United States

Bachelor's Degree

Computer Science, Data Engineering, or related field

Required

Master's Degree

Computer Science, Data Engineering, or related field

Preferred

5+ Years of Prior Relevant Experience

ML/AI engineering, Data Science, or Analytics Engineering

Required

Ability to provide technical leadership in MLOps strategies and pipeline standardization.

Required

Guides teams on best practices for leveraging AI automation tools.

Required

Directly impacts reliability, scalability, and efficiency of ML/AI solutions.

Required

Prior experience supporting or partnering with marketing, revenue, or operations analytics teams.

Preferred

Familiarity with TensorFlow, PyTorch, Scikit-learn, or other neural network and machine learning frameworks.

Preferred

Expertise in ML/AI pipeline development and lifecycle automation.

Strong proficiency in Python and PySpark.

Experience with cloud platforms (Databricks, AWS, GCP, Azure).

Proficiency in containerization (Docker, Kubernetes) and orchestration tools (Airflow, Kubeflow, MLflow).

Knowledge of CI/CD for ML workflows and version control systems (Git, DVC).

Strong experience in monitoring, logging, and automated retraining frameworks.

Balancing

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Carrying 10 pounds

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Clear speech - simple

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Communication - verbal

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Contacts - works alone

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Contacts - works around others

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Contacts - works with others

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Hazardous conditions - chemicals

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Hazardous conditions - high structures

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Hazardous conditions - high voltage

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Noise levels - high

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