AI Machine Learning 1-FFPP-8967

Onyx Point•Hanover, MD

9d•$78,000 - $250,000

About The Position

The AI/ML Engineer designs, creates, tests, and productizes AI/ML algorithms to solve business challenges. Models should learn and make predictions according to business logic developed to meet customer requirements. The role covers model architecture, interaction with data pipelines, and applying, interpreting, and presenting metrics. It also requires familiarity with application development, infrastructure management, data engineering, and data governance. The engineer trains, retrains, deploys, schedules, monitors, and improves models through iterative user and system feedback to create scalable, high-performing solutions. The role may include leading geographically diverse teams and serving as a primary point of contact for AI-related matters. For this position, the engineer builds, trains, fine-tunes, evaluates, and deploys advanced language models, with particular emphasis on NLP, Marian, and multilingual model development. The role includes model evaluation and benchmarking, error analysis and debugging, optimization, deployment on cloud-based AI platforms—especially AWS SageMaker—and integration into production systems. The engineer also contributes to front-end and back-end development for AI applications and collaborates with data scientists, product teams, and engineers to turn business requirements into technical solutions. Workflows, model designs, and technical specifications are documented. An active TS/SCI with Full Scope Polygraph clearance and U.S. citizenship are required. The salary range is $78,000 to $250,000 per year; the exact amount depends on work location, role, skills, and expertise. The position offers flexible work arrangements, including remote work options.

Requirements

Active TS/SCI security clearance with Full Scope Polygraph; U.S. citizenship required.
At least 2 years of applied machine-learning experience on programs and contracts of similar scope, type, and complexity.
Bachelor of Science degree in advanced mathematics (such as calculus, linear algebra, or Bayesian statistics), computer science, or a related STEM discipline from an accredited college or university; alternatively, 3 additional years of machine-learning experience on projects with similar processes may substitute for the degree.
Strong proficiency with AI/ML frameworks such as TensorFlow, PyTorch, or Hugging Face.
Expertise in model training, evaluation, and deployment.
Hands-on experience with AWS SageMaker, Lambda, EC2, and S3.
Experience developing full-stack software applications using languages such as JavaScript, Python, and Java.
Strong Java, C, and C++ programming experience.
Solid understanding of data structures, algorithms, and system design.
Extensive machine-learning model-development experience, including NLP.
Experience with model evaluation, optimization, and performance monitoring.
Proven software-engineering experience and strong coding skills.
Experience with language models, including Marian and multilingual model development.
Experience working with open-source model libraries, Deep Learning Containers, GPU technologies, and optimization or tuning.
Experience with model versioning and management using tools such as MLflow or Git.
Familiarity with containerization tools such as Kubernetes.
Strong analytical and problem-solving skills, and excellent communication and collaboration abilities.

Nice To Haves

Knowledge of computational linguistics.
Willingness to support occasional on-call duties.
Experience in a government or defense-related environment.
Experience with secure data handling and compliance requirements.
Experience working with large language models (LLMs).
Expert knowledge of multiple programming languages, such as Python, Java, C, and R.

Responsibilities

Build, train, and fine-tune machine-learning models, particularly language models.
Apply model-training, tuning, and optimization best practices and design solutions to improve model performance.
Evaluate models through performance analysis, benchmarking, error analysis, debugging, and diagnostics.
Deploy and scale models using cloud-based AI platforms, especially AWS SageMaker.
Integrate machine-learning models into production environments and other systems.
Contribute to front-end and back-end development and maintenance for AI model-based applications.
Collaborate with software engineers on scalable and efficient deployment pipelines.
Work with data scientists, product teams, and engineers to translate business requirements into technical solutions.
Document workflows, model design processes, and technical specifications.
Select appropriate datasets, perform statistical analysis, run machine-learning algorithms, and use results to improve models.
Train and retrain systems as needed, use ML libraries and packages, run standard test and evaluation protocols, and provide system integration oversight.
May lead geographically diverse teams and serve as a primary point of contact for AI-related matters.