AI/ML Engineer

GliaCell Technologies•Annapolis Junction, MD

7d•$115,000 - $190,000

About The Position

GliaCell Technologies focuses on Software & System Engineering in Enterprise and Cyber Security solution spaces. We excel at delivering stable and reliable software solutions using Agile Software Development principles. These provide us the capability to deliver a quick turn-around using interactive applications and the integration of industry standard software stacks. GliaCell’s Enterprise capabilities include Full-Stack Application Development, Big Data, Cloud Technologies, Analytics, Machine Learning, AI, and DevOps Containerization. We also provide customer solutions in the areas of CND, CNE, and CNO by providing our customers with assessments and solutions in Threat Mitigation, Vulnerability Exposure, Penetration Testing, Threat Hunting, and Preventing Advanced Persistent Threat. GliaCell is seeking an AI/ML Engineer on one of our subcontracts. This is a full-time position offering the opportunity to support a U.S. Government customer. The mission is to provide technical expertise that assists in sustaining critical mission-related software and systems to a large government contract. Seeking an AI/ML Engineer, responsible for building, training, fine-tuning, and evaluating advanced language models. The ideal candidate will have hands-on experience with state-of-the-art machine learning techniques, specifically for natural language processing (NLP), and will also have a strong understanding of full-stack development. This person will work across the model development lifecycle and collaborate with cross-functional teams to deliver high-impact solutions.

Requirements

Possess an active or rein-statable TS/SCI with Polygraph security clearance. (This position requires in-scope poly, within 7 years).
U.S. Citizenship.
2+ years experience in applied machine learning in programs and contracts of similar scope, type, and complexity is required. A B.S. degree in advanced math (e.g., calculus, linear algebra or Bayesian statistics), computer science or related STEM discipline from an accredited college or university is required. 3 years of additional machine learning experience on projects with similar machine learning processes may be substituted for a bachelor’s degree.
Experience with standard machine language frameworks, e.g. Pytorch, TensorFlow.
Work well independently as well as on a team.
Strong communication skills.
Knowledge and experience of Language models is required
Specific experience with Marian and multi-lingual model development
Experience with Open-Source model libraries, Deep Learning Containers (DLC), GPU technologies and optimization / tuning
Strong Java, C, C++ programming experience
Strong proficiency in AI/ML frameworks such as TensorFlow, PyTorch, or Hugging Face.
Expertise in model training, evaluation, and deployment.
Hands-on experience with AWS tools like SageMaker, Lambda, EC2, and S3.
Experience in developing full-stack software applications (JavaScript, Python, Java, etc.).
Solid understanding of data structures, algorithms, and system design.
Extensive experience in machine learning model development, including natural language processing (NLP).
Experience with model evaluation, optimization, and performance monitoring.
Proven experience in software engineering with strong coding skills.

Nice To Haves

Knowledge of Computation Linguistics is a plus
Willingness to support occasional on-call duties is a plus
Experience working in a government or defense-related environment is highly preferred.
Experience with model versioning and management using tools such as MLFlow or Git.
Familiarity with containerization tools (i.e. Kubernetes).
Strong problem-solving and analytical skills.
Excellent communication and collaboration abilities.
Experience with secure data handling and compliance requirements
Experience working with Large Language Models (LLMs)

Responsibilities

Build, train, and fine-tune machine learning models, particularly language models
Apply best practices in model training, tuning, and optimization.
Design and implement solutions for model performance improvement.
Conduct rigorous model evaluation, including performance analysis and benchmarking.
Perform error analysis, debugging, and model diagnostics to ensure quality and reliability.
Work with cloud-based AI platforms (especially AWS Sagemaker) to deploy and scale models.
Integrate machine learning models into production environments, ensuring seamless integration with other systems.
Contribute to the development and maintenance of the full stack for AI model-based applications (front-end and back-end).
Collaborate with software engineers to build scalable and efficient deployment pipelines.
Work closely with data scientists, product teams, and engineers to translate business requirements into technical solutions.
Document workflows, model design processes, and technical specifications.