SummaryKarsun Solutions is actively looking to support a modernization of digital services and accelerate delivery of healthcare and benefits programs for Veterans at the Department of Veterans Affairs. Candidates will support a program focused on expanding the Digital Transformation Center's ability to deliver enterprise applications using modern development frameworks, low code and no code platforms, and cloud based SaaS and PaaS environments. The objective is to deliver digital capabilities that support both Veteran healthcare services and benefits programs.
The program supports multiple concurrent low code/no code products serving a wide-range of users, while maintaining reliability, security, and delivery speed across a complex enterprise platform environment.
This position is a contingent opportunity. Prefer candidates local to the DMV Area and willing to be onsite two days per week.What You'll Be Doing:- Design, develop, deploy, and maintain scalable machine learning models and pipelines that support enterprise mission objectives and operational baselines.
- Apply advanced ML frameworks and lifecycle management practices to improve predictive capability, automation, and analytical insight, aligned with business requirements and data governance.
- Lead model integration with the DTC enterprise platforms (Salesforce/MuleSoft, Microsoft Power Platform/Dynamics, Pega, Box, Appian) through APIs and reusable components.
- Lead model performance monitoring, including drift detection, retraining, and validation, so solutions stay reliable and compliant after deployment.
- Protect PII and PHI in training data and model outputs under HIPAA and VA privacy requirements, and support security and ATO documentation for ML components.
- Educate stakeholders and provide technical guidance so solutions are delivered on schedule.
Required Qualifications:- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Information Systems, Operations Research, or a related field, plus a minimum of 7 years of relevant experience. 10 years of relevant experience may substitute for the degree.
- Demonstrated experience designing, deploying, and maintaining machine learning models and pipelines in production enterprise environments.
- Ability to obtain and maintain a VA Public Trust background investigation.
- Available on contract award and willing to sign a letter of commitment.
Preferred Qualifications:- Experience with MLOps tooling and model lifecycle management in a commercial cloud environment, such as AWS SageMaker or Azure Machine Learning.
- Experience with ML in healthcare, benefits, or other regulated environments, including FedRAMP or ATO processes.
- Proficiency in Python and common ML frameworks, such as PyTorch, TensorFlow, or scikit-learn.
- Experience with data governance and responsible AI practices.
Salary RangeThe proposed salary range for this role is $160,000 to $180,000 USD. The salary range provided is a good faith estimate representative of all experience levels. Karsun considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills.
Third Party ResumesKarsun does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Karsun and Karsun will not be obligated to pay a placement fee.
Clearance InformationThis position requires the eligibility to obtain a security clearance. The Defense Industrial Security Clearance Office (DISCO), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website: https://www.state.gov/m/ds/clearances/c10978.htm
LocationTo be considered for this role, you must reside in one of the following states: CA, CO, DC, FL, GA, IL, MD, NJ, NY, NC, OH, OK, PA, SC, TX, VA.
Work AuthorizationApplicants must be authorized to work in the U.S.
