Artificial Intelligence/Machine Learning (AI/ML) Engineer – Skill Level 1 Specialization: Natural Language Processing (NLP), Multilingual Language Models & AI/ML Development Required Clearance: Active TS/SCI with Full Scope Polygraph (in scope within the past 7 years) Hourly Pay Range: $20.00–$50.00 Wyetech is seeking an Artificial Intelligence/Machine Learning (AI/ML) Engineer Level 1 to support the design, development, training, fine-tuning, evaluation, deployment, and sustainment of advanced machine learning models, with a particular emphasis on Natural Language Processing (NLP), multilingual language models, and AI-driven applications. The successful candidate will be responsible for building and optimizing language models, including Marian-based machine translation and multilingual model development, while supporting the complete machine learning lifecycle. This position requires hands-on experience with modern AI/ML frameworks, open-source model libraries, deep learning containers, GPU technologies, and cloud-based machine learning platforms. The AI/ML Engineer will collaborate with data scientists, software engineers, product teams, and mission stakeholders to develop scalable solutions that meet operational requirements. Responsibilities include model architecture design, training and retraining, statistical analysis, performance benchmarking, deployment, production integration, and ongoing optimization. The ideal candidate will possess strong programming experience in Java, C, and C++, familiarity with TensorFlow, PyTorch, or Hugging Face, and experience deploying AI/ML models using AWS services such as SageMaker, Lambda, EC2, and S3. A strong understanding of full-stack software development, data engineering, infrastructure management, and secure model deployment is also important. Required Skills/Experience
- Hands-on experience developing, training, fine-tuning, evaluating, and deploying machine learning models, particularly language models and NLP solutions.
- Demonstrated knowledge of language model architectures, training methodologies, optimization techniques, and model evaluation practices.
- Experience with Marian and multilingual language model development, including machine translation or multilingual NLP applications.
- Experience working with open-source machine learning model libraries and frameworks.
- Experience using deep learning containers (DLCs) to support model development, training, and deployment.
- Experience with GPU technologies, including GPU-based model training, performance optimization, and tuning.
- Strong programming experience in Java, C, and C++.
- Proficiency with AI/ML frameworks such as TensorFlow, PyTorch, or Hugging Face.
- Experience developing, training, retraining, testing, and optimizing machine learning models.
- Experience selecting appropriate datasets and performing statistical analysis to support model development.
- Experience evaluating model accuracy, performance, reliability, and operational effectiveness.
- Experience conducting error analysis, debugging, benchmarking, and model diagnostics.
- Experience using cloud-based AI/ML platforms, particularly AWS SageMaker.
- Familiarity with AWS services such as Lambda, EC2, and S3 for model development, deployment, and infrastructure integration.
- Experience integrating machine learning models into production applications and operational systems.
- Experience supporting full-stack software development, including frontend and backend application components.
- Experience developing software applications using JavaScript, Python, Java, or comparable programming languages.
- Strong understanding of data structures, algorithms, software architecture, and system design.
- Experience collaborating with software engineers to develop scalable model deployment pipelines.
- Experience working with cross-functional teams to translate operational requirements into technical AI/ML solutions.
- Experience documenting model architectures, development workflows, evaluation results, and technical specifications.
- Strong analytical, troubleshooting, communication, and problem-solving skills.
- Active TS/SCI clearance with Full Scope Polygraph completed within the past seven (7) years.
Desired Skills/Experience
- Knowledge of computational linguistics and its application to natural language processing and multilingual model development.
- Experience developing or supporting Large Language Models (LLMs).
- Experience working in Government, Department of Defense, or Intelligence Community environments.
- Experience with secure data handling, data governance, and compliance requirements.
- Experience using MLflow, Git, or comparable tools for model versioning, experiment tracking, and lifecycle management.
- Experience using Kubernetes or comparable container orchestration technologies.
- Experience implementing machine learning operations (MLOps) practices and automated deployment pipelines.
- Experience optimizing GPU resource utilization for model training and inference.
- Experience improving multilingual model performance through data preprocessing, fine-tuning, and evaluation.
- Experience developing scalable cloud-native AI/ML applications.
- Experience supporting model monitoring, drift detection, and performance improvements in production environments.
- Experience developing and maintaining automated model training and evaluation workflows.
- Familiarity with infrastructure management, data engineering, and data pipeline integration.
- Experience supporting geographically distributed technical teams.
- Willingness to participate in occasional on-call support as needed.
Responsibilities and Capabilities AI/ML Model Development and Training
- Design, develop, implement, and maintain machine learning algorithms and models to address mission and operational requirements.
- Build, train, fine-tune, and optimize language models, with particular emphasis on NLP and multilingual applications.
- Develop and enhance Marian-based machine translation and multilingual language model solutions.
- Select appropriate datasets to support model training, validation, and evaluation.
- Apply statistical analysis and machine learning methodologies to identify patterns, evaluate performance, and improve model effectiveness.
- Implement model architectures and training approaches that meet established functional and performance requirements.
- Train and retrain models using appropriate datasets, optimization techniques, and evaluation feedback.
- Utilize open-source machine learning frameworks, libraries, and packages to accelerate development.
- Apply best practices for model tuning, optimization, and performance improvement.
- Develop scalable AI/ML solutions capable of learning and generating predictions consistent with customer requirements.
Natural Language Processing and Multilingual Model Development
- Develop and maintain NLP solutions supporting language understanding, translation, and multilingual processing.
- Support model development and optimization using Marian and related language model technologies.
- Evaluate multilingual model performance across supported languages and datasets.
- Apply appropriate preprocessing, tokenization, training, and fine-tuning methodologies.
- Analyze model outputs to identify translation errors, performance deficiencies, and opportunities for improvement.
- Collaborate with technical stakeholders to establish language model performance requirements.
- Support improvements to model accuracy, efficiency, and operational reliability.
- Investigate and recommend emerging NLP techniques and open-source model technologies.
Model Evaluation, Testing, and Performance Optimization
- Execute standard AI/ML test and evaluation protocols.
- Conduct model benchmarking, performance analysis, and statistical evaluation.
- Perform error analysis, debugging, and model diagnostics to identify performance limitations.
- Evaluate model accuracy, reliability, efficiency, and operational effectiveness.
- Analyze evaluation results and implement improvements to model architectures, training methodologies, and inference processes.
- Support GPU optimization and tuning to improve training and inference performance.
- Document model evaluation methodologies, test results, and recommended improvements.
- Implement iterative model improvements based on user feedback, system performance, and evaluation findings.
- Monitor model performance and recommend retraining or configuration adjustments when necessary.
Cloud-Based Model Deployment and Integration
- Develop and deploy AI/ML models using cloud-based platforms, particularly AWS SageMaker.
- Utilize AWS services such as Lambda, EC2, and S3 to support model development, deployment, storage, and integration.
- Integrate trained models into production applications and operational environments.
- Support the deployment, scheduling, monitoring, and maintenance of AI/ML models.
- Collaborate with software engineering and infrastructure teams to establish reliable model deployment workflows.
- Support containerized AI/ML environments using deep learning containers and related technologies.
- Assist with model scaling, infrastructure optimization, and production performance improvements.
- Provide system integration oversight to ensure AI/ML solutions operate effectively with supporting applications and infrastructure.
- Troubleshoot model deployment, compatibility, performance, and integration issues.
Full-Stack Software Engineering
- Contribute to the development and maintenance of frontend and backend components supporting AI/ML applications.
- Develop software components using Java, C, C++, Python, JavaScript, or other applicable programming languages.
- Collaborate with software engineers to integrate AI/ML models into scalable application architectures.
- Support the design and implementation of interfaces connecting model services with enterprise applications.
- Apply software engineering principles to improve application reliability, performance, and maintainability.
- Support automated build, testing, and deployment workflows.
- Troubleshoot software defects, integration failures, and application performance issues.
- Assist with maintaining technical documentation for AI/ML applications and supporting software components.
Data Engineering, Model Lifecycle, and Governance
- Work with data engineering teams to identify and prepare appropriate datasets for model training and evaluation.
- Support data pipeline integration and ensure data is available for model development activities.
- Apply statistical methods to evaluate data quality, model outputs, and performance metrics.
- Support model versioning, configuration management, and experiment tracking.
- Maintain documentation covering model architectures, datasets, training procedures, evaluation results, and deployment configurations.
- Support retraining and model improvement activities based on changing operational requirements.
- Apply applicable secure data handling and governance practices.
- Assist with maintaining traceability of model changes and evaluation results throughout the model lifecycle.
Technical Collaboration and Reporting
- Collaborate with data scientists, software engineers, infrastructure teams, product teams, and mission stakeholders.
- Translate operational and customer requirements into AI/ML technical solutions.
- Participate in model design reviews, technical discussions, testing activities, and integration planning.
- Communicate model performance results, technical findings, risks, and recommended improvements.
- Prepare and maintain technical documentation, development workflows, and model evaluation reports.
- Provide technical guidance regarding AI/ML capabilities and integration considerations.
- Support coordination among geographically distributed technical teams when required.
- Participate in occasional on-call support activities as needed.
- Perform additional AI/ML Engineering responsibilities to support program and mission objectives.
Qualifications
Candidates must meet the Artificial Intelligence/Machine Learning Engineer Skill Level 1 (38561) labor category requirements through one of the following pathways: Option 1 – Bachelor's Degree
- Bachelor's degree in Advanced Mathematics, Computer Science, or a related Science, Technology, Engineering, or Mathematics (STEM) discipline from an accredited college or university.
- Relevant advanced mathematics coursework may include calculus, linear algebra, Bayesian statistics, or comparable subjects.
- Two (2) years of applied machine learning experience supporting programs and contracts of similar scope, type, and complexity.
OR Option 2 – Experience Substitution
- Five (5) years of relevant applied machine learning experience supporting programs and contracts of similar scope, type, and complexity.
- Three (3) additional years of qualifying machine learning experience on projects with similar machine learning processes may be substituted for the bachelor's degree requirement.
Additional Position-Specific Qualifications
- Demonstrated experience developing, training, fine-tuning, and evaluating language models.
- Experience with Marian and multilingual language model development.
- Experience using open-source AI/ML libraries, deep learning containers, and GPU technologies.
- Strong programming skills in Java, C, and C++.
- Experience with TensorFlow, PyTorch, Hugging Face, or comparable AI/ML frameworks.
- Experience with AWS SageMaker and related cloud services.
- Experience supporting model deployment, integration, and production sustainment.
- Familiarity with full-stack software development and application integration.
- Strong understanding of algorithms, data structures, and system design.
- Excellent technical communication, analytical, and problem-solving abilities.
Security Clearance and Additional Requirements
- Required Clearance: Active TS/SCI with Full Scope Polygraph.
- Polygraph Requirement: Must possess an in-scope Full Scope Polygraph completed within the past seven (7) years.
- U.S. Citizenship required.
- Willingness to support occasional on-call responsibilities is preferred.
- Must maintain applicable security clearance and program access requirements throughout employment.
At 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. Wyetech 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.
Additional benefits include:
Wyetech 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. Full-time employees have the option to participate in a variety of voluntary benefit plans including:
- A Choice of Medical Plan Options, some with Health Savings Account (HSA)
- Vision and Dental
- Life and AD&D Benefits
- Short and Long-Term Disability
- Hospital Indemnity, Accident, and Critical Illness Insurances
- Optional Identity Theft and Legal Protection Services
- Employee Referral Bonus Eligibility up to $10,000
- Mobility Among Wyetech-supported Contracts
- Various contract and work locations throughout Maryland, Virginia, Colorado, Texas, Utah, Alaska, Hawaii and OCONUS
- Various team-building events throughout the year, such as monthly lunches, summer company picnic, and an annual holiday party.
- Employees receive two complimentary branded clothing orders annually.
Hourly Pay Range: $20.00–$50.00 Hourly pay rates listed for this position serve as a general guideline and are not a guarantee of compensation. Compensation will vary dependent upon factors including but not limited to: Government contract rates; education; relevant prior work experience, knowledge, skills, and competencies; certifications; and geographic location. Hourly pay rates reflect the pre-benefit gross wage amounts. Wyetech, LLC is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Affirmative Action Statement:
Wyetech, LLC is committed to the principles of affirmative action in all hiring and employment for minorities, women, individuals with disabilities, and protected veterans.
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Wyetech, LLC is committed to providing an inclusive and accessible hiring process. If you need any accommodations during the application or interview process, please contact Brittney Wood. at 844-WYETECH x727 or staffing@wyetech.com. We are happy to provide reasonable accommodations to ensure equal access to all candidates.
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