Posted 2d ago

AI/ML Technical Lead

@ GCubed
Fort Belvoir, Virginia, United States
OnsiteFull Time
Responsibilities:designing models, building pipelines, monitoring models
Requirements Summary:Active secret clearance, Bachelor's in a related field, 3+ years ML/AI/data science or software engineering experience, strong Python, experience with ML frameworks and production deployments, cloud and data tooling, and strong communication skills.
Technical Tools Mentioned:Python, SQL, R, PyTorch, TensorFlow, Scikit-learn, XGBoost, AWS, AWS SageMaker, Microsoft Azure, Microsoft Azure AI, Google Cloud, Google Vertex AI, Docker, Kubernetes, MLflow, Airflow, CI/CD, Pandas, NumPy, Spark, Snowflake, Databricks, Hugging Face, LangChain, OpenAI API, vector databases, Ask Sage
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Job Description

Job Purpose

GCubed, Inc. is seeking an AI/ML Engineer to design, build, and deploy machine learning models and AI-powered solutions that solve real business problems. This role will work closely with engineering, product, data, and business teams to turn complex data into scalable, practical, and measurable solutions. The ideal candidate has strong technical skills, curiosity, problem-solving ability, and experience with Large Language Models (LLMs).

Essential Functions of the Job

• Design, develop, train, and deploy machine learning and AI models.

• Build scalable ML pipelines for data processing, model training, testing, and deployment.

• Work with structured and unstructured data, including text, images, documents, and large datasets.

• Collaborate with data engineers, software engineers, and product teams to integrate AI/ML solutions into applications.

• Evaluate model performance and improve accuracy, efficiency, reliability, and scalability.

• Research and apply current AI/ML methods, tools, and best practices.

• Support development of predictive models, recommendation systems, NLP tools, automation workflows, and generative AI solutions.

• Monitor deployed models and troubleshoot issues related to drift, bias, performance, and data quality.

• Document model architecture, assumptions, limitations, and performance metrics.

• Ensure responsible AI practices, including privacy, security, fairness, and compliance.