AI/ML Engineer (R-00194)
Required skills
- Generative AI
- Foundation Models
- RAG Architectures
- Embeddings
- Vector Search
- Semantic Retrieval
- Prompt Engineering
- LLM Evaluation
- Supervised Machine Learning
- Unsupervised Machine Learning
Role summary
True Zero Technologies is seeking an AI/ML Engineer to design, build, deploy, and operate AI/ML capabilities within secure government cloud environments, specifically AWS GovCloud using FedRAMP-authorized services. The role involves the full AI/ML lifecycle, from data preparation to ongoing optimization, with a strong emphasis on security, compliance, and Zero Trust principles. Responsibilities include integrating AI/ML solutions for mission and business workflows, developing models, and ensuring their evaluation and monitoring. Experience with Generative AI, foundation models, RAG, embeddings, vector search, prompt engineering, traditional ML, MLOps, containerization, and responsible AI concepts is required. Familiarity with government or security-sensitive environments is also necessary.
The AI/ML Engineer is responsible for designing, building, integrating, deploying, and operating artificial intelligence and machine learning capabilities that support mission and business workflows in secure government cloud environments. This role spans the AI/ML lifecycle, including data preparation, model and application development, evaluation, deployment, monitoring, and ongoing optimization.
The engineer will leverage FedRAMP-authorized services available within AWS GovCloud, while ensuring all AI/ML solutions align with applicable security, data handling, compliance, and Zero Trust requirements. The ideal candidate has strong Python development skills, experience with modern AI/ML architectures and data pipelines, and practical knowledge of deploying and evaluating models in cloud environments.
Job Responsibilities
AI/ML Engineering & Mission Integration
- 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 that expose 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 to determine whether proposed AI/ML technologies are appropriate for the mission, security environment, and available GovCloud services.
- 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 supporting data processing, inference, evaluation, and system integration.
- Apply standard software engineering practices including source control, automated testing, code review, dependency management, and CI/CD.
- Develop and integrate machine learning models, foundation models, or AI services based on approved use cases and architecture.
- Optimize AI/ML application performance, reliability, scalability, and resource utilization.
- Troubleshoot issues involving model behavior, data quality, application integration, cloud services, and runtime environments.
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- Design and implement Generative AI and Retrieval-Augmented Generation (RAG) patterns when included within the approved solution scope.
- Develop workflows for document ingestion, parsing, chunking, embedding generation, indexing, retrieval, prompt construction, and model inference.
- 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 overall solution effectiveness.
- Develop prompt-management, model-routing, and orchestration patterns where appropriate.
- Implement safeguards and validation mechanisms to reduce the risk of inappropriate, inaccurate, or unauthorized model outputs.
- Support secure integration of vector stores, search services, knowledge repositories, and other components required by RAG architectures.
Python & AI/ML Development
Generative AI & Retrieval-Augmented Generation
Job Qualifications
- AWS Certified AI Practitioner
- AWS Certified Machine Learning Specialty
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