AI ML Engineer
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Job Location:
Blue Ash, OH - USA
Monthly Salary:
Not Disclosed
Posted on:
2 hours ago
Vacancies:
1 Vacancy
Job Summary
2 Candidate Submittal Slots New High Level Policy
**Please strictly adhere to the following resume naming convention:
ALL CAPS NO SPACES B/T UNDERSCORES
Bill Rate $70.00 - $80.00
PTNUSGBAMSREQIDCandidateBeelineID
i.e. PTNUS9999999SKIPJOHNSON0413
MSP Owner: Rob Finton
Location: Blue Ash OH
Duration: 6 months
GBaMS ReqID:
Competencies: 10 years experience required
Agile Way of Working
Digital : Machine Learning
Digital : Artificial Intelligence(AI)
Role Description:
ML Engineering Delivery
o Lead the design and implementation of production ML pipelines for training batch inference and real-time/near-real-time scoring.
o Translate Data Science prototypes into robust maintainable services and workflows with strong testing observability and reliability.
o Build and manage feature engineering workflows feature stores (where applicable) and reusable ML components.
o Drive model packaging and deployment patterns (containers serverless managed endpoints) and optimize for performance and cost.
MLOps
o Implement CICD for ML (model versioning automated testing promotion gates rollback strategies) using Azure DevOps GitHub Actions integrated with Databricks
o Leverage MLflow (Databricks native) for experiment tracking model registry and lifecycle managementt
o Establish best practices for model monitoring data drift concept drift model degradation and alerting.
o Define and enforce guardrails for responsible AI bias checks explainability privacy controls and auditability.
Data Platform Collaboration
o Partner with Data Engineering on data quality lineage and availability to ensure reliable model inputs.
o Work with CloudPlatform teams to ensure scalable infrastructure (compute networking IAM secrets logging).
oI nfluence target architecture and technology decisions for the ML platform roadmap.
Leadership Mentoring
o Provide technical leadership and mentorship to ML Engineers and junior team members.
o Conduct design reviews code reviews and establish engineering standards.
o Coordinate delivery plans estimate work and manage technical risks and dependencies.
REQUIRED SKILLS:
Languages Python (required) SQL optional JavaScala
MLMLOps MLflow (or equivalent) model registry monitoring evaluation pipelines
Data Spark DataFrames data modeling fundamentals feature engineering
DevOps Git CICD Docker Kubernetes Terraform (optional)
Cloud Azure logging/monitoring
Experience with MLOps practices including model versioning monitoring and CICD for ML pipelines.
Desirable Skills: Knowledge of Retail domain
GOOD TO HAVE:
Understanding of Data Science models
Exposure to Deep Learning frameworks such as TensorFlow or PyTorch
Solid understanding of feature engineering model evaluation and experimentation.
PREFERRED TRAITS:
Strong communication and storytelling skills with data
Ability to work in a collaborative and fast-paced environment
Passion for solving complex business problems using data
**Please strictly adhere to the following resume naming convention:
ALL CAPS NO SPACES B/T UNDERSCORES
Bill Rate $70.00 - $80.00
PTNUSGBAMSREQIDCandidateBeelineID
i.e. PTNUS9999999SKIPJOHNSON0413
MSP Owner: Rob Finton
Location: Blue Ash OH
Duration: 6 months
GBaMS ReqID:
Competencies: 10 years experience required
Agile Way of Working
Digital : Machine Learning
Digital : Artificial Intelligence(AI)
Role Description:
ML Engineering Delivery
o Lead the design and implementation of production ML pipelines for training batch inference and real-time/near-real-time scoring.
o Translate Data Science prototypes into robust maintainable services and workflows with strong testing observability and reliability.
o Build and manage feature engineering workflows feature stores (where applicable) and reusable ML components.
o Drive model packaging and deployment patterns (containers serverless managed endpoints) and optimize for performance and cost.
MLOps
o Implement CICD for ML (model versioning automated testing promotion gates rollback strategies) using Azure DevOps GitHub Actions integrated with Databricks
o Leverage MLflow (Databricks native) for experiment tracking model registry and lifecycle managementt
o Establish best practices for model monitoring data drift concept drift model degradation and alerting.
o Define and enforce guardrails for responsible AI bias checks explainability privacy controls and auditability.
Data Platform Collaboration
o Partner with Data Engineering on data quality lineage and availability to ensure reliable model inputs.
o Work with CloudPlatform teams to ensure scalable infrastructure (compute networking IAM secrets logging).
oI nfluence target architecture and technology decisions for the ML platform roadmap.
Leadership Mentoring
o Provide technical leadership and mentorship to ML Engineers and junior team members.
o Conduct design reviews code reviews and establish engineering standards.
o Coordinate delivery plans estimate work and manage technical risks and dependencies.
REQUIRED SKILLS:
Languages Python (required) SQL optional JavaScala
MLMLOps MLflow (or equivalent) model registry monitoring evaluation pipelines
Data Spark DataFrames data modeling fundamentals feature engineering
DevOps Git CICD Docker Kubernetes Terraform (optional)
Cloud Azure logging/monitoring
Experience with MLOps practices including model versioning monitoring and CICD for ML pipelines.
Desirable Skills: Knowledge of Retail domain
GOOD TO HAVE:
Understanding of Data Science models
Exposure to Deep Learning frameworks such as TensorFlow or PyTorch
Solid understanding of feature engineering model evaluation and experimentation.
PREFERRED TRAITS:
Strong communication and storytelling skills with data
Ability to work in a collaborative and fast-paced environment
Passion for solving complex business problems using data
