Artificial Intelligence Machine Learning Skill Level 2

Job Description

Job Description:\n\nArtificial Intelligence/Machine Learning (AI/ML) Engineer – Skill Level 2 \nRequired Clearance: Active TS Clearance with Customer Appropriate Polygraph Hourly Pay Range: $20.00–$50.00\nWyetech is seeking an experienced Artificial Intelligence/Machine Learning (AI/ML) Engineer Level 2 to support the research, design, development, testing, evaluation, integration, deployment, and sustainment of advanced AI/ML algorithms and scalable machine learning solutions supporting mission-critical Government requirements.\nThe successful candidate will develop and optimize machine learning models capable of identifying patterns, learning from data, and generating predictions aligned with customer requirements. This position requires experience across the machine learning lifecycle, including dataset selection, statistical analysis, model architecture, training and retraining, performance evaluation, integration, deployment, and ongoing optimization.\nThe AI/ML Engineer will research and implement AI/ML algorithms, evaluate data and model quality, transform data science prototypes into scalable operational solutions, and oversee iterative testing and validation activities. The selected candidate will also support data pipeline integration, software architecture considerations, data modeling, and the identification of changes in data distributions that may affect model performance.\nThe ideal candidate will possess experience with standard machine learning frameworks such as PyTorch and TensorFlow, strong analytical and problem-solving capabilities, and the ability to collaborate effectively with software engineers, data scientists, systems engineers, and mission stakeholders.Required Skills/Experience\n\n \n Demonstrated experience designing, developing, testing, evaluating, and deploying artificial intelligence and machine learning algorithms.\n \n Experience supporting applied machine learning initiatives in programs or contracts of similar scope, type, and complexity.\n \n Experience selecting appropriate datasets for model training, testing, and evaluation.\n \n Experience performing statistical analysis to identify patterns, evaluate datasets, and support model development.\n \n Experience implementing and executing machine learning algorithms.\n \n Experience using model evaluation results to improve model performance, accuracy, reliability, and efficiency.\n \n Experience training, retraining, fine-tuning, and optimizing machine learning models.\n \n Experience working with machine learning libraries, frameworks, and software packages.\n \n Experience using standard machine learning frameworks such as PyTorch and TensorFlow.\n \n Experience conducting model testing, evaluation, verification, and validation.\n \n Experience overseeing iterative AI/ML testing and evaluation activities.\n \n Experience researching and implementing machine learning algorithms, methodologies, and supporting tools.\n \n Experience selecting appropriate data and knowledge representation methods.\n \n Understanding of software architecture, data modeling, data structures, and system integration.\n \n Experience transforming data science prototypes into scalable software solutions.\n \n Experience evaluating data quality and verifying machine learning model outputs.\n \n Experience identifying differences in data distributions that affect model performance.\n \n Experience supporting integration of AI/ML models into existing applications and operational environments.\n \n Familiarity with data pipelines, infrastructure management, data engineering, and data governance.\n \n Experience documenting model architectures, development processes, evaluation results, and technical findings.\n \n Strong analytical, troubleshooting, communication, and problem-solving skills.\n \n Ability to collaborate with technical teams and stakeholders to translate requirements into effective AI/ML solutions.\n \n Active TS/SCI clearance with Full Scope Polygraph completed within the past seven (7) years.\nDesired Skills/Experience\n\n \n Strong programming experience in Python, Java, C, R, or comparable languages.\n \n Experience developing scalable machine learning solutions within enterprise or mission-critical environments.\n \n Experience supporting AI/ML model deployment, monitoring, and lifecycle management.\n \n Experience optimizing model performance and computational efficiency.\n \n Experience developing or maintaining automated model training and evaluation workflows.\n \n Experience integrating AI/ML capabilities with enterprise applications, databases, and data processing systems.\n \n Experience working with large datasets and complex data processing pipelines.\n \n Experience supporting machine learning model versioning, configuration management, and experiment tracking.\n \n Familiarity with cloud-based AI/ML development and deployment environments.\n \n Experience implementing data quality assessments and monitoring changes in data distributions.\n \n Experience supporting AI/ML verification and validation activities.\n \n Experience coordinating technical activities across geographically distributed engineering teams.\n \n Experience serving as a technical point of contact for AI/ML development and integration activities.\n \n Experience supporting Government, Department of Defense, or Intelligence Community programs.\nResponsibilities and Capabilities\nAI/ML Model Development and Training\n\n \n Design, develop, implement, and maintain artificial intelligence and machine learning models to meet customer and mission requirements.\n \n Select appropriate datasets for model development, training, validation, and evaluation.\n \n Perform statistical analysis to assess data characteristics, identify patterns, and support model development.\n \n Implement and execute machine learning algorithms using appropriate development frameworks and tools.\n \n Train, retrain, fine-tune, and optimize machine learning models based on operational requirements and evaluation results.\n \n Apply machine learning methodologies to improve model accuracy, efficiency, scalability, and reliability.\n \n Research and implement a broad range of AI/ML algorithms and tools.\n \n Evaluate alternative model architectures and recommend solutions that meet established functional and performance requirements.\n \n Develop scalable machine learning solutions capable of supporting operational workloads.\n \n Document model development processes, training methodologies, configurations, and technical decisions.\n\nModel Testing, Evaluation, Verification, and Validation\n\n \n Execute standard AI/ML testing and evaluation protocols.\n \n Oversee testing and evaluation of AI/ML algorithms through an iterative design process.\n \n Ensure model testing activities support established verification and validation requirements.\n \n Conduct model performance assessments, benchmarking, and statistical evaluations.\n \n Analyze model outputs to identify errors, performance limitations, and opportunities for improvement.\n \n Evaluate model accuracy, reliability, computational efficiency, and operational effectiveness.\n \n Verify data quality and model output quality.\n \n Document evaluation methodologies, testing results, deficiencies, and recommended corrective actions.\n \n Implement improvements based on model testing, validation results, and user feedback.\n \n Support ongoing model performance monitoring and evaluation activities.\n\nData Analysis, Modeling, and Representation\n\n \n Select and prepare appropriate datasets to support AI/ML development and evaluation.\n \n Perform statistical analysis to evaluate dataset quality, characteristics, and suitability.\n \n Design or select appropriate data and knowledge representation methods.\n \n Apply data modeling and data structure principles to support machine learning solutions.\n \n Analyze data distributions and identify changes that may affect model accuracy or reliability.\n \n Support data preprocessing, transformation, and integration activities.\n \n Collaborate with data engineering teams to ensure appropriate data pipeline interaction.\n \n Evaluate model inputs and outputs to identify inconsistencies, anomalies, or performance concerns.\n \n Recommend improvements to data processing and representation methodologies.\n\nSoftware Architecture and Prototype Modernization\n\n \n Transform data science prototypes into scalable, maintainable, and operationally effective software solutions.\n \n Apply software architecture principles to support AI/ML system development.\n \n Evaluate data structures, data models, application interfaces, and system dependencies.\n \n Collaborate with software engineers to integrate machine learning models into enterprise applications.\n \n Support development of scalable model deployment and integration architectures.\n \n Analyze technical requirements and recommend appropriate software and infrastructure solutions.\n \n Assist with improving model performance, reliability, and maintainability.\n \n Support software integration, testing, troubleshooting, and operational validation activities.\n\nModel Deployment, Integration, and Sustainment\n\n \n Provide system integration oversight for AI/ML models and supporting software components.\n \n Support the deployment, scheduling, monitoring, and maintenance of machine learning solutions.\n \n Integrate trained models into operational applications and existing technical environments.\n \n Collaborate with systems engineers, software developers, and infrastructure personnel to resolve integration issues.\n \n Monitor deployed models and identify opportunities for performance improvements.\n \n Support retraining and model updates based on changing requirements, data characteristics, and evaluation results.\n \n Troubleshoot model integration, compatibility, and performance issues.\n \n Maintain documentation supporting model deployment, configuration, integration, and sustainment.\n \n Recommend improvements to model lifecycle management and operational support procedures.\n\nAI/ML Research and Technical Innovation\n\n \n Research and evaluate emerging AI/ML algorithms, methodologies, frameworks, and tools.\n \n Assess the suitability of new technologies for mission-specific applications.\n \n Develop and test prototype AI/ML solutions to evaluate technical feasibility and performance.\n \n Recommend improvements to existing model architectures and development approaches.\n \n Investigate opportunities to improve computational efficiency, scalability, and operational effectiveness.\n \n Collaborate with technical stakeholders to identify innovative AI/ML solutions.\n \n Document research findings, technical evaluations, and recommendations.\n\nTechnical Collaboration and Documentation\n\n \n Collaborate with data scientists, software engineers, systems engineers, infrastructure teams, and mission stakeholders.\n \n Translate customer requirements into technical AI/ML development and integration activities.\n \n Participate in model design reviews, technical planning meetings, testing activities, and integration discussions.\n \n Communicate model performance findings, technical challenges, and recommended solutions.\n \n Prepare and maintain technical documentation covering model architectures, datasets, training procedures, evaluation results, and deployment configurations.\n \n Support technical coordination across geographically distributed engineering teams as required.\n \n Serve as a technical resource for AI/ML development, integration, and sustainment activities.\n \n Perform additional AI/ML Engineering responsibilities as required to support mission objectives.\nQualifications\nCandidates must meet the Artificial Intelligence/Machine Learning Engineer Skill Level 2 (38562) labor category requirements and the position-specific experience requirement.\nOption 1 – Master's or Doctoral Degree\n\n \n Master's degree or Ph.D. in Advanced Mathematics, Artificial Intelligence, Data Science, Computer Science, Deep Learning, or a related qualifying discipline from an accredited college or university.\n \n Five (5) years of applied machine learning experience supporting programs and contracts of similar scope, type, and complexity.\n \n Experience with standard machine learning frameworks such as PyTorch and TensorFlow.\n\nOR\nOption 2 – Bachelor's Degree and Experience Substitution\n\n \n Bachelor's degree in a relevant technical discipline from an accredited college or university.\n \n Ten (10) years of qualifying applied machine learning experience supporting programs and contracts of similar scope, type, and complexity.\n \n Five (5) additional years of qualifying machine learning experience may be substituted for the master's degree requirement.\n \n Experience with standard machine learning frameworks such as PyTorch and TensorFlow.\n\nAdditional Position-Specific Qualifications\n\n \n Demonstrated experience developing, testing, evaluating, and deploying AI/ML algorithms.\n \n Experience selecting datasets, performing statistical analysis, and training machine learning models.\n \n Experience conducting model testing, verification, validation, and performance evaluation.\n \n Experience researching and implementing AI/ML algorithms and tools.\n \n Understanding of software architecture, data modeling, and data structures.\n \n Experience transforming data science prototypes into scalable solutions.\n \n Experience evaluating data and model output quality.\n \n Ability to identify data distribution changes affecting model performance.\n \n Strong technical communication, analytical, and problem-solving skills.\nSecurity Clearance and Additional Requirements\n\n \n Required Clearance: Active TS Clearance with Customer Appropriate Polygraph\n \n U.S. Citizenship required.\n \n Must maintain applicable security clearance and program access requirements throughout employment.\n\nAt Wyetech, you’ll be at the center of an award-winning corporate culture, breaking technological barriers and solving real-world problems for our federal government customers. We are committed to hiring the best of the best, and in return, we offer a world-class, truly unique employee experience that is rare within our industry.\nWyetech believes in generously supporting employees as they prepare for retirement. The company automatically contributes 20% of each employee's gross compensation to a Simplified Employee Pension (SEP) IRA, with no requirement for employee matching. All contributions are fully vested from day one, ensuring immediate ownership of retirement funds.\nAdditional benefits include:\nWyetech provides a generous PTO plan of up to 200 hours annually, aligned with applicable state leave regulations. Employees have the flexibility to adjust their PTO allocation at the start of each calendar year, ensuring it meets their evolving needs.\nFull-time employees have the option to participate in a variety of voluntary benefit plans including:\n\n \n A Choice of Medical Plan Options, some with Health Savings Account