Machine Learning (ML) Engineer

Command Holdings•Tampa, FL

1d•Onsite

About The Position

WWC Global, an operating firm of Command Holdings, is seeking a Machine Learning (ML) Engineer to serve on a potential contract supporting USSOCOM's mission to transform the SOF Enterprise into a decisive data-centric organization by applying scientific methods, algorithms, processes, and systems to extract actionable insights and knowledge from both structured and unstructured data, enabling the full spectrum of analysis, including descriptive, diagnostic, predictive, and prescriptive analytics.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related technical field.
Minimum of five (5) years of experience in machine learning engineering, data science, or a related technical discipline, including hands-on experience designing, training, deploying, and monitoring ML models in cloud or hybrid production environments.
Current, active TS/SCI security clearance.
Outstanding communication skills, influencing abilities, and client focus.
Professional proficiency in English is required.
Demonstrated proficiency in using all Microsoft Office applications.
Ability to access federal facilities in compliance with Real ID.
Applicants must be currently authorized to work in the United States on a full-time basis.
WWC Global will not sponsor applicants for work visas for this position.

Nice To Haves

Master's degree or PhD in Machine Learning, Computer Science, Artificial Intelligence, Applied Mathematics, or a related field.
Experience working in DoD or Intelligence Community environments.
Experience developing and deploying AI/ML capabilities in support of SOF or other warfighting organizations.
Experience with LLM/RAG development and GenAI operationalization in classified or restricted environments.
Experience conducting AI/ML experimentation and multi-lateral exercises to validate model performance against high-impact use cases.
Experience with low-bandwidth and Disconnected, Intermittent, and Limited (DIL) environment constraints.
One or more of the following: Databricks Certified Machine Learning Professional, AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate, TensorFlow Developer Certificate, or equivalent AI/ML platform certification.

Responsibilities

Designing, developing, deploying, and maintaining machine learning models and AI-enabled systems that support SOF Enterprise data analysis, operational planning, and decision advantage.
Developing AI/ML algorithms and models including those designed for Natural Language Processing (NLP), computer vision, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and decision-making that learn from data to identify patterns and improve performance over time.
Building and managing end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
Conducting AI/ML modeling and simulation efforts to create models that analyze data, recognize patterns, and make predictions, and using those models to simulate real-world scenarios to test various outcomes without real-world risk.
Implementing MLOps practices to ensure scalable, repeatable, and auditable model development and deployment workflows.
Conducting suitability and feasibility assessments to confirm AI/ML is the appropriate tool for a given task and establishing clear requirements and fully specified tasks prior to development.
Ensuring all LLM/RAG experimentation and model development adheres to mandatory security and data governance requirements defined by Federal, DoW, and SOCOM policies.
Designing and testing analytics and AI-enabled solutions side-by-side with SOF stakeholders and demonstrating capabilities via robust campaigns of learning.
Identifying and mitigating operational environment constraints, such as limited bandwidth at the tactical edge and contested environments early in the development process.
Supporting the establishment of clear pathways to adopt capabilities deemed successful after rigorous testing to ensure sustainment and wide-scale adoption.
Utilizing appropriate code share repositories (e.g., GitHub) for all development activities.