AI/ML Engineer

True Zero Technologies, LLC

AI/ML Engineer building secure Generative AI and RAG solutions in AWS GovCloud for True Zero Technologies. Developing Python services, model integrations, evaluation frameworks, and responsible AI safeguards for government missions.

Posted 8/14/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and integrating AI/ML solutions using Python, with a focus on Generative AI and RAG architectures. Proficient in deploying and managing machine learning models within secure environments, ensuring compliance with responsible AI practices.

Highest-signal resume keywords

Python DevelopmentGenerative AIMachine Learning Model TrainingMLOps PracticesAWS Certified Machine Learning Specialty

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Hard Skills

AI/ML Solution DevelopmentData PreparationAPI DevelopmentModel EvaluationFeature EngineeringContainerized WorkloadsAutomated TestingModel SelectionPrompt EngineeringVector Search

Soft Skills

CollaborationProblem-SolvingTechnical Documentation

Tools & Technologies

AWS GovCloudCI/CDMicroservicesVector StoresKnowledge Repositories

Certifications & Qualifications

AWS Certified AI PractitionerAWS Certified Machine Learning Specialty

Industry Keywords

CybersecurityGovernmentDefenseRegulated EnvironmentsResponsible AI

Tech Stack

Tools & technologies

AWSCloudCyber SecurityMicroservicesPython

About the role

Key responsibilities & impact

Build and integrate AI/ML capabilities supporting mission workflows using FedRAMP-authorized services available in AWS GovCloud, from data preparation through deployment, evaluation, and monitoring
Design, develop, test, and operationalize AI/ML solutions aligned to defined mission and business requirements
Translate operational use cases into scalable AI/ML architectures, services, and integration patterns
Develop APIs, services, and application integrations exposing AI/ML capabilities to mission applications and enterprise platforms
Collaborate with application engineers, data engineers, cloud engineers, cybersecurity teams, and mission stakeholders throughout the solution lifecycle
Conduct technical evaluations and proof-of-concept implementations of AI/ML technologies
Maintain technical documentation covering architecture, model behavior, interfaces, dependencies, deployment procedures, and operational requirements
Develop production-quality AI/ML applications and supporting services using Python
Build reusable Python modules, services, utilities, and automation for data processing, inference, evaluation, and system integration
Apply source control, automated testing, code review, dependency management, and CI/CD
Develop and integrate machine learning models, foundation models, or AI services
Optimize AI/ML application performance, reliability, scalability, and resource utilization
Troubleshoot model behavior, data quality, application integration, cloud services, and runtime issues
Design and implement Generative AI and Retrieval-Augmented Generation (RAG) patterns
Develop document ingestion, parsing, chunking, embedding, indexing, retrieval, prompt construction, and model inference workflows
Integrate approved large language models and foundation-model services with enterprise applications and mission data sources
Evaluate retrieval quality, response relevance, groundedness, hallucination risk, and solution effectiveness
Develop prompt-management, model-routing, and orchestration patterns
Implement safeguards and validation mechanisms to reduce inappropriate, inaccurate, or unauthorized model outputs
Support secure integration of vector stores, search services, knowledge repositories, and other RAG components

Requirements

What you’ll need

Bachelor’s degree in Cybersecurity, Computer Science, Information Technology, Information Systems, or a related technical discipline
Experience with Generative AI, foundation models, and RAG architectures, where applicable to the program scope
Experience with embeddings, vector search, semantic retrieval, prompt engineering, and LLM evaluation
Experience with supervised or unsupervised machine learning, feature engineering, model training, and model selection where traditional ML is in scope
Familiarity with MLOps or LLMOps practices for model and application lifecycle management
Experience developing automated model and application evaluation frameworks
Familiarity with responsible AI concepts, including model limitations, bias evaluation, explainability, traceability, and human oversight
Experience deploying containerized workloads and microservices
Experience working in government, defense, regulated, or other security-sensitive environments
Preferred certifications: AWS Certified AI Practitioner; AWS Certified Machine Learning Specialty

Benefits

Comp & perks

Competitive salary, paid twice per month
Best in class medical coverage
100% of medical premiums covered by True Zero
Company wide new business incentive programs
Contribution Incentives (i.e. white papers, blog posts, internal webinars, etc.)
3 weeks of PTO starting + 11 Paid Holidays Annually
401k Program with 100% company match on the first 4%
Monthly reimbursement of Cell Phone and Home Internet costs
Paternity/Maternity Leave
Investment in training and certifications to broaden and deepen your technical skills