Artificial Intelligence Machine Learning Skill Level 1

Wyetech•Annapolis Junction, MD

1d•$20 - $50

About The Position

Wyetech is seeking an Artificial Intelligence/Machine Learning Engineer, Skill Level 1, specializing in Natural Language Processing (NLP), multilingual language models, and AI/ML development. The engineer will support the design, development, training, fine-tuning, evaluation, deployment, and sustainment of machine learning models, with particular emphasis on Marian-based machine translation and multilingual model development. The position supports the full machine learning lifecycle, including model architecture design, statistical analysis, performance benchmarking, production integration, and ongoing optimization. The role works with data scientists, software engineers, product teams, infrastructure teams, and mission stakeholders to create scalable solutions that meet operational requirements. It involves open-source machine learning frameworks and libraries, deep learning containers, GPU technologies, and cloud-based platforms, particularly AWS SageMaker and related services. The engineer will also contribute to full-stack software development, data engineering, model lifecycle management, secure deployment, technical documentation, and production support. Candidates must meet the Skill Level 1 labor-category qualifications through either a bachelor's degree in advanced mathematics, computer science, or a related STEM discipline and two years of applied machine learning experience, or an experience-substitution pathway requiring five years of relevant applied machine learning experience, with three additional years of qualifying experience substituting for the bachelor's requirement. An active TS/SCI clearance with an in-scope Full Scope Polygraph completed within the past seven years and U.S. citizenship are required. Occasional on-call support may be needed. The position offers an hourly pay range of $20.00–$50.00. Wyetech describes its culture as focused on technological problem-solving for federal government customers and offers a retirement contribution, PTO, optional benefits, referral bonuses, contract mobility, team events, and other employee offerings.

Requirements

Active TS/SCI clearance with a Full Scope Polygraph completed within the past seven years; the polygraph must be in scope.
U.S. citizenship.
Meet one of the stated Skill Level 1 qualification pathways: a bachelor's degree in advanced mathematics, computer science, or a related STEM discipline plus two years of applied machine learning experience; or five years of relevant applied machine learning experience, with three additional years of qualifying experience substituting for the bachelor's requirement.
Hands-on experience developing, training, fine-tuning, evaluating, and deploying machine learning models, particularly language models and NLP solutions.
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 with open-source machine learning libraries and frameworks and deep learning containers.
Experience with GPU-based model training, performance optimization, and tuning.
Strong programming experience in Java, C, and C++.
Proficiency with TensorFlow, PyTorch, Hugging Face, or comparable AI/ML frameworks.
Experience selecting datasets and conducting statistical analysis for model development; evaluating accuracy, performance, reliability, and operational effectiveness; and conducting error analysis, debugging, benchmarking, and diagnostics.
Experience with cloud-based AI/ML platforms, particularly AWS SageMaker, and familiarity with AWS Lambda, EC2, and S3.
Experience integrating machine learning models into production applications and operational systems.
Experience supporting full-stack software development, including frontend and backend components, and developing applications using JavaScript, Python, Java, or comparable languages.
Strong understanding of data structures, algorithms, software architecture, and system design.
Experience collaborating with software engineers on scalable model deployment pipelines and working with cross-functional teams to translate operational requirements into technical solutions.
Experience documenting model architectures, development workflows, evaluation results, and technical specifications.
Strong analytical, troubleshooting, communication, and problem-solving skills.
Must maintain applicable security clearance and program access requirements throughout employment.

Nice To Haves

Knowledge of computational linguistics and its application to NLP and multilingual model development.
Experience developing or supporting Large Language Models (LLMs).
Experience in Government, Department of Defense, or Intelligence Community environments.
Experience with secure data handling, data governance, and compliance requirements.
Experience with MLflow, Git, or comparable model-versioning, experiment-tracking, and lifecycle-management tools.
Experience with Kubernetes or comparable container orchestration technologies.
Experience implementing MLOps practices and automated deployment pipelines.
Experience optimizing GPU resource utilization for training and inference.
Experience improving multilingual model performance through preprocessing, fine-tuning, and evaluation.
Experience developing scalable cloud-native AI/ML applications.
Experience with production model monitoring, drift detection, and performance improvement.
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.

Responsibilities

Design, develop, implement, maintain, train, fine-tune, and optimize machine learning algorithms and models for mission and operational requirements.
Develop NLP solutions for language understanding, translation, and multilingual processing, including Marian-based machine translation and multilingual language models.
Select and prepare datasets for training, validation, and evaluation; apply statistical analysis to assess data quality and model performance.
Evaluate model accuracy, reliability, efficiency, and operational effectiveness through testing, benchmarking, error analysis, debugging, and diagnostics.
Improve model architectures, training methods, inference processes, and GPU performance based on evaluation findings and user feedback.
Develop and deploy AI/ML models using cloud platforms, particularly AWS SageMaker, and use AWS Lambda, EC2, and S3 for development, deployment, storage, and integration.
Integrate trained models into production applications and operational environments; support deployment, scheduling, monitoring, maintenance, scaling, and troubleshooting.
Support containerized AI/ML environments using deep learning containers and related technologies.
Contribute to frontend and backend software components, interfaces, application architectures, and automated build, test, and deployment workflows.
Work with data engineering teams on datasets and data pipeline integration; support model versioning, configuration management, experiment tracking, retraining, and lifecycle traceability.
Apply secure data handling and governance practices and maintain documentation on model architectures, datasets, training, evaluation, deployment, and technical specifications.
Collaborate with technical and mission stakeholders to translate operational requirements into AI/ML solutions; participate in design reviews, testing, and integration planning.
Communicate model performance, technical findings, risks, and recommendations; prepare technical documentation and evaluation reports.
Support coordination among geographically distributed technical teams and participate in occasional on-call activities as needed.
Perform additional AI/ML engineering responsibilities in support of program and mission objectives.

Benefits

Wyetech automatically contributes 20% of each employee's gross compensation to a Simplified Employee Pension (SEP) IRA, with no employee matching requirement; contributions are fully vested from day one.
PTO plan of up to 200 hours annually, aligned with applicable state leave regulations, with flexibility to adjust PTO allocation at the start of each calendar year.
Full-time employees may participate in voluntary medical plan options, some with a Health Savings Account (HSA).
Vision and dental benefits.
Life and AD&D benefits.
Short- and long-term disability benefits.
Hospital indemnity, accident, and critical illness insurance options.
Optional identity theft and legal protection services.
Employee referral bonus eligibility up to $10,000.
Mobility among Wyetech-supported contracts.
Team-building events, including monthly lunches, a summer company picnic, and an annual holiday party.
Two complimentary branded clothing orders annually.