Kforce Technology Staffing
Machine Learning Engineer
Kforce Technology Staffing, Los Angeles, California, United States, 90079
Responsibilities
Production Deployment and Model Engineering: Deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability
Scalable ML Infrastructures: Developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform, or Azure
Engineering Leadership: Lead engineering efforts in creating and implementing methods and workflows for ML/GenAI model engineering, LLM advancements, and optimizing deployment frameworks while aligning with business strategic directions
AI Pipeline Development: Developing AI pipelines for various data processing needs, including data ingestion, preprocessing, and search and retrieval, ensuring solutions meet all technical and business requirements
Collaboration: Collaborate with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines for continuous improvement of machine learning models
CI/CD Pipelines: Implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes
Monitoring and Logging: Setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance
Version Control: Implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration
Security and Compliance: Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations
Documentation: Maintaining clear and comprehensive documentation of ML Ops processes and configurations
Requirements
Bachelor’s degree Computer Science, Artificial Intelligence, Informatics or closely related field
Master’s degree in Computer Science, Engineering or closely related field preferred
3 years of relevant Machine Learning Engineer experience
Healthcare Expertise: Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems, including integrating machine learning models with these systems
Experience in managing end-to-end ML lifecycle
Experience in managing automation with Terraform
Deep understanding of coding, architecture, and deployment processes
Strong understanding of critical performance metrics
Extensive experience in predictive modeling, LLMs, and NLP
Ability to effectively articulate the advantages and applications of the RAG framework with LLMs
Proven experience with
Artificial intelligence and machine learning platforms (e.g., AWS, Azure or Google Cloud Platform)
Containerization technologies (e.g., Docker) or container orchestration platforms (e.g., Kubernetes)
CI/CD tools (e.g., Github Actions)
Programming languages and frameworks (e.g., Python, R, SQL)
MLOps engineering principles, agile methodologies, and DevOps life-cycle management
Technical writing and documentation for AI/ML models and processes
Healthcare data and machine learning use cases
Benefits and additional information The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.
We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.
Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce’s sole discretion unless and until paid and may be modified in its discretion consistent with the law.
This job is not eligible for bonuses, incentives or commissions.
Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.
By clicking ?Apply Today? you agree to receive calls, AI-generated calls, text messages or emails from Kforce and its affiliates, and service providers. Note that if you choose to communicate with Kforce via text messaging the frequency may vary, and message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You will always have the right to cease communicating via text by using key words such as STOP.
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Production Deployment and Model Engineering: Deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability
Scalable ML Infrastructures: Developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform, or Azure
Engineering Leadership: Lead engineering efforts in creating and implementing methods and workflows for ML/GenAI model engineering, LLM advancements, and optimizing deployment frameworks while aligning with business strategic directions
AI Pipeline Development: Developing AI pipelines for various data processing needs, including data ingestion, preprocessing, and search and retrieval, ensuring solutions meet all technical and business requirements
Collaboration: Collaborate with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines for continuous improvement of machine learning models
CI/CD Pipelines: Implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes
Monitoring and Logging: Setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance
Version Control: Implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration
Security and Compliance: Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations
Documentation: Maintaining clear and comprehensive documentation of ML Ops processes and configurations
Requirements
Bachelor’s degree Computer Science, Artificial Intelligence, Informatics or closely related field
Master’s degree in Computer Science, Engineering or closely related field preferred
3 years of relevant Machine Learning Engineer experience
Healthcare Expertise: Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems, including integrating machine learning models with these systems
Experience in managing end-to-end ML lifecycle
Experience in managing automation with Terraform
Deep understanding of coding, architecture, and deployment processes
Strong understanding of critical performance metrics
Extensive experience in predictive modeling, LLMs, and NLP
Ability to effectively articulate the advantages and applications of the RAG framework with LLMs
Proven experience with
Artificial intelligence and machine learning platforms (e.g., AWS, Azure or Google Cloud Platform)
Containerization technologies (e.g., Docker) or container orchestration platforms (e.g., Kubernetes)
CI/CD tools (e.g., Github Actions)
Programming languages and frameworks (e.g., Python, R, SQL)
MLOps engineering principles, agile methodologies, and DevOps life-cycle management
Technical writing and documentation for AI/ML models and processes
Healthcare data and machine learning use cases
Benefits and additional information The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.
We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.
Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce’s sole discretion unless and until paid and may be modified in its discretion consistent with the law.
This job is not eligible for bonuses, incentives or commissions.
Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.
By clicking ?Apply Today? you agree to receive calls, AI-generated calls, text messages or emails from Kforce and its affiliates, and service providers. Note that if you choose to communicate with Kforce via text messaging the frequency may vary, and message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You will always have the right to cease communicating via text by using key words such as STOP.
#J-18808-Ljbffr