Job Requirements
Huntsville, AL
Secret Polygraph not specified
Early Career (2+ yrs experience)
Salary not specified
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Job Description
Position Overview
We are recruiting an AI/ML Engineer for a direct placement opportunity in Huntsville, Alabama. This position supports the design, development, integration, and assurance of emerging artificial intelligence and machine learning technologies used to harden and secure mission-critical defense systems.
The engineer will contribute to trusted, robust AI/ML solutions that interpret complex datasets, predict outcomes, and automate decision-making in support of military platforms across ground, missile defense, space, and installation infrastructure science and technology programs.
This is a consolidated opportunity covering multiple technical tracks. Depending on program needs and experience, assignments may emphasize one or a combination of Classic Machine Learning and Predictive Modeling, LLM and Generative AI Applications, AI Assurance and Responsible AI, Edge AI/ML Deployment, and hardening AI/ML systems against adversarial threats.
Across these areas, the engineer will conduct independent research, collaborate with cross-functional technical teams, and clearly communicate complex technical work and results to stakeholders.
Responsibilities
Design, develop, and maintain machine learning models ranging from classical algorithms, including regression, tree ensembles, and clustering, to modern deep learning architectures.
Build LLM-enabled applications and retrieval-augmented generation (RAG) pipelines, including vector embedding generation, vector database integration, and prompt and context engineering.
Develop AI assurance and evaluation tooling, including robustness testing, bias and fairness analysis, model traceability, red-team and adversarial testing, and audit artifact generation.
Optimize and deploy models for production and edge environments using techniques such as quantization, compression, containerized inference, ONNX, and TensorRT.
Implement secure model and data pipelines, defend against adversarial machine learning threats, and support software supply-chain integrity, including Software Bills of Materials (SBOMs) for AI components.
Conduct data processing and analysis to improve model accuracy.
Document and present development processes, technical results, and AI assurance evidence to stakeholders.
Contribute to Agile, team-based planning and estimating in a fast-paced, collaborative engineering environment.
Required Qualifications
Bachelor’s degree or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related field.
Proven experience in one or more of the following areas: ML/LLM development, AI assurance and evaluation tooling, or deployment of edge computing solutions.
Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Proficiency in Python and at least one additional programming language, such as Java or C++.
Strong understanding of data structures, data modeling, and software architecture.
Highlighted Skills & Experience
Modern AI
LLM frameworks and application development.
Vector embeddings and vector databases.
Retrieval-augmented generation architectures.
Prompt and context engineering.
LLM fine-tuning.
Model evaluation and benchmarking.
Classic Machine Learning
Feature engineering.
Model selection and tuning.
Statistical analysis.
Predictive modeling across structured and unstructured data.
AI Assurance
Responsible AI practices, including robustness, bias and fairness, and traceability.
Adversarial machine learning defenses.
Red-team testing.
Auditability and compliance documentation.
Edge AI & Deployment
ONNX and TensorRT.
Model quantization and compression.
ARM, NVIDIA Jetson, and DSP targets.
Containerized inference.
MLOps in controlled or classified environments.
Platform & DevSecOps
REST APIs.
CI/CD for software and machine learning systems.
SAST and DAST.
Infrastructure-as-Code.
Cloud platforms, including AWS, Azure, and GCP.
Domain Experience
Familiarity with computer networking.
Secure system integration for mission platforms.
Test and verification and validation support using Python or MATLAB.
Contributions to open-source AI/ML projects.
Experience deploying AI models in production, classified, or embedded environments.
Understanding of computer security principles and secure software development lifecycle practices.
About the Opportunity
This direct placement opportunity provides the chance to work on emerging AI/ML technologies supporting complex national defense applications. The role spans areas including predictive modeling, LLM and GenAI applications, RAG, AI assurance, adversarial machine learning, edge deployment, MLOps, and secure AI system integration.
The AI/ML Engineer will contribute to the development of trusted, auditable, robust, and mission-ready AI capabilities while working across multidisciplinary engineering teams supporting complex defense systems.
We are recruiting an AI/ML Engineer for a direct placement opportunity in Huntsville, Alabama. This position supports the design, development, integration, and assurance of emerging artificial intelligence and machine learning technologies used to harden and secure mission-critical defense systems.
The engineer will contribute to trusted, robust AI/ML solutions that interpret complex datasets, predict outcomes, and automate decision-making in support of military platforms across ground, missile defense, space, and installation infrastructure science and technology programs.
This is a consolidated opportunity covering multiple technical tracks. Depending on program needs and experience, assignments may emphasize one or a combination of Classic Machine Learning and Predictive Modeling, LLM and Generative AI Applications, AI Assurance and Responsible AI, Edge AI/ML Deployment, and hardening AI/ML systems against adversarial threats.
Across these areas, the engineer will conduct independent research, collaborate with cross-functional technical teams, and clearly communicate complex technical work and results to stakeholders.
Responsibilities
Design, develop, and maintain machine learning models ranging from classical algorithms, including regression, tree ensembles, and clustering, to modern deep learning architectures.
Build LLM-enabled applications and retrieval-augmented generation (RAG) pipelines, including vector embedding generation, vector database integration, and prompt and context engineering.
Develop AI assurance and evaluation tooling, including robustness testing, bias and fairness analysis, model traceability, red-team and adversarial testing, and audit artifact generation.
Optimize and deploy models for production and edge environments using techniques such as quantization, compression, containerized inference, ONNX, and TensorRT.
Implement secure model and data pipelines, defend against adversarial machine learning threats, and support software supply-chain integrity, including Software Bills of Materials (SBOMs) for AI components.
Conduct data processing and analysis to improve model accuracy.
Document and present development processes, technical results, and AI assurance evidence to stakeholders.
Contribute to Agile, team-based planning and estimating in a fast-paced, collaborative engineering environment.
Required Qualifications
Bachelor’s degree or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, Data Science, or a related field.
Proven experience in one or more of the following areas: ML/LLM development, AI assurance and evaluation tooling, or deployment of edge computing solutions.
Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Proficiency in Python and at least one additional programming language, such as Java or C++.
Strong understanding of data structures, data modeling, and software architecture.
Highlighted Skills & Experience
Modern AI
LLM frameworks and application development.
Vector embeddings and vector databases.
Retrieval-augmented generation architectures.
Prompt and context engineering.
LLM fine-tuning.
Model evaluation and benchmarking.
Classic Machine Learning
Feature engineering.
Model selection and tuning.
Statistical analysis.
Predictive modeling across structured and unstructured data.
AI Assurance
Responsible AI practices, including robustness, bias and fairness, and traceability.
Adversarial machine learning defenses.
Red-team testing.
Auditability and compliance documentation.
Edge AI & Deployment
ONNX and TensorRT.
Model quantization and compression.
ARM, NVIDIA Jetson, and DSP targets.
Containerized inference.
MLOps in controlled or classified environments.
Platform & DevSecOps
REST APIs.
CI/CD for software and machine learning systems.
SAST and DAST.
Infrastructure-as-Code.
Cloud platforms, including AWS, Azure, and GCP.
Domain Experience
Familiarity with computer networking.
Secure system integration for mission platforms.
Test and verification and validation support using Python or MATLAB.
Contributions to open-source AI/ML projects.
Experience deploying AI models in production, classified, or embedded environments.
Understanding of computer security principles and secure software development lifecycle practices.
About the Opportunity
This direct placement opportunity provides the chance to work on emerging AI/ML technologies supporting complex national defense applications. The role spans areas including predictive modeling, LLM and GenAI applications, RAG, AI assurance, adversarial machine learning, edge deployment, MLOps, and secure AI system integration.
The AI/ML Engineer will contribute to the development of trusted, auditable, robust, and mission-ready AI capabilities while working across multidisciplinary engineering teams supporting complex defense systems.
group id: 91172415