Amida Technology Solutions · 4 hours ago
Machine Learning Engineer
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Responsibilities
Design, build, and optimize end-to-end machine learning pipelines for training, testing, deployment, and monitoring
Collaborate closely with Data Scientists and Platform Teams to bring a variety of data-centric projects into our production environment
Integrate data preprocessing, feature engineering, model training, and model evaluation workflows into scalable solutions
Develop and implement frameworks for automated testing of data preprocessing, model accuracy, and pipeline robustness
Perform root cause analysis of pipeline failures and implement solutions to prevent recurrence
Establish standards for version control, reproducibility, and model validation to ensure consistency across deployments
Design and deploy monitoring tools to track model performance and pipeline efficiency in production environments
Identify and address performance bottlenecks in pipelines, ensuring optimal resource utilization
Utilize AzureML for efficient scaling of ML models, applying best practices in version control, CI/CD, MLflow, and lifecycle management
Manage and maintain the Microsoft Azure cloud infrastructure, services, and solutions relevant to AI/ML operations
Understand and apply infrastructure-as-code principles using tools like Terraform and Azure build pipelines
Work closely with data engineers and software engineers to ensure seamless integration with existing data and software systems
Maintain an incident response plan for AI pipeline issues to minimize downtime
Qualification
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Required
Bachelor's or master's degree in computer science, Engineering, or a related field
7+ years of experience in Azure Cloud engineering with a strong focus on AzureML, Databricks, PysSpark
Proficiency in Python and machine learning frameworks
Expertise in Python and YAML languages pertaining to AI/ML workflows, with proficiency in additional scripting languages (e.g., Bash, PowerShell)
Hands-on experience with data pipeline tools such as Apache Airflow, Kubeflow, or MLflow.
Familiarity with Azure DevOps (ADO), CI/CD practices, and Azure AI/ML services
Expertise in version control tools and CI/CD practices
Strong foundation in AI/ML principles, with practical experience in deploying models at scale
Strong understanding of data preprocessing, schema enforcement, feature engineering, and model evaluation techniques
Experience in implementing testing frameworks for machine learning models and pipelines.
Strong experience with data lake, lake house, and databases
Excellent problem-solving and analytical thinking abilities
Strong communication skills to explain complex concepts to non-technical stakeholders
Ability to work both independently and collaboratively in a fast-paced environment
Familiarity with agile process
Must obtain Public Trust Clearance
Preferred
Familiarity with containerization technologies (Docker, Kubernetes, OpenShift)
Familiarity with catalog tools (purview, Collibra)
Experience with monitoring tools such as Prometheus+Grafana, Datadog, Dynatrace
Knowledge of data governance and compliance frameworks in AI, such as HIPAA
Company
Amida Technology Solutions
Amida Technology Solutions, Inc.
Funding
Current Stage
Growth StageRecent News
2024-04-15
2024-04-15
2024-04-15
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