Amida Technology Solutions, Inc.
Machine Learning Engineer
Amida Technology Solutions, Inc., Washington, District of Columbia, us, 20022
Amida Technology Solutions is a DC-based software services company focused on the most difficult problems in data interoperability, exchange, governance, and security. We design, develop, and deploy software solutions that collect, reconcile, and transform data for business intelligence, predictive analytics, and decision support. We specialize in taking data from inception to impact.
Our team is comprised of creative, forward thinkers who are passionate about using cutting-edge technology to make a difference in people's lives and have a positive impact on our country. We offer an entrepreneurial, high-growth environment that values fresh ideas, candid conversations, and authentic teamwork.
Amida is seeking a highly skilled and detail-oriented
Machine Learning Engineer
to design, implement, and maintain robust machine learning pipelines while ensuring the highest standards of quality throughout the AI development lifecycle. The ideal candidate will have a strong background in machine learning, data engineering, and quality assurance practices, with a passion for building scalable, production-ready AI solutions.
What you will do: 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. Required Skills: 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. Preferred Skills: 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. Must obtain Public Trust Clearance. Communication is the key to success at Amida. Our people are known for their can-do attitude and their ability to work effectively with client teams. We pride ourselves on having a collegial, multidisciplinary team with diverse backgrounds and experience. Our best team members pay intense attention to detail in all aspects of their work, have a strong sense of initiative, are opinionated about the best ways of doing things, and align quickly to decisions. A sense of humor is an asset at Amida. We help solve the biggest challenges in data management. If you are looking for an opportunity to help important organizations get their data right, we hope you forward your resume and let us know why this role appeals to you. We look forward to hearing from you. Amida Technology Solutions is an equal opportunity employer, and all qualified applicants will receive consideration without regard to race, color, religion, gender, national origin, disability status, protected Veteran status, or any other characteristic protected by law. We value diversity of ideas and people.
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Machine Learning Engineer
to design, implement, and maintain robust machine learning pipelines while ensuring the highest standards of quality throughout the AI development lifecycle. The ideal candidate will have a strong background in machine learning, data engineering, and quality assurance practices, with a passion for building scalable, production-ready AI solutions.
What you will do: 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. Required Skills: 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. Preferred Skills: 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. Must obtain Public Trust Clearance. Communication is the key to success at Amida. Our people are known for their can-do attitude and their ability to work effectively with client teams. We pride ourselves on having a collegial, multidisciplinary team with diverse backgrounds and experience. Our best team members pay intense attention to detail in all aspects of their work, have a strong sense of initiative, are opinionated about the best ways of doing things, and align quickly to decisions. A sense of humor is an asset at Amida. We help solve the biggest challenges in data management. If you are looking for an opportunity to help important organizations get their data right, we hope you forward your resume and let us know why this role appeals to you. We look forward to hearing from you. Amida Technology Solutions is an equal opportunity employer, and all qualified applicants will receive consideration without regard to race, color, religion, gender, national origin, disability status, protected Veteran status, or any other characteristic protected by law. We value diversity of ideas and people.
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