AI / ML Engineer

Benz Technology

Fort Meade (MD)

On-site

USD 120,000 - 180,000

Full time

14 days+
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Job summary

Benz Technology is seeking an experienced AI/ML Solutions Engineer to design, develop, and deploy LLM-powered tooling and analytics within governed cloud environments. You will own end-to-end from data engineering to production, integrating AI into front-end experiences and software analysis systems.

Responsibilities span designing RAG frameworks, data pipelines, and containerized workloads on AWS and Kubernetes, with strong emphasis on governance, auditable deployments, and Agile collaboration

Qualifications

  • Experience designing and deploying LLM-powered applications.
  • Strong data engineering and Python development skills.
  • Familiarity with RAG architectures and model orchestration.
  • Hands-on experience with cloud-native tooling (AWS, Kubernetes).
  • Knowledge of AI governance and auditable deployments.

Responsibilities

  • Design and implement LLM-powered applications and RAG frameworks.
  • Develop data engineering workflows using Python and PySpark.
  • Deploy containerized AI workloads on Kubernetes and AWS (SageMaker, EKS).
  • Integrate AI capabilities with static analysis and automated testing systems.
  • Build front-end analytics experiences using React and AWS Amplify.
  • Maintain AI/ML governance, versioning, and auditable processes.
  • Collaborate with customers and engineering teams using Agile practices.

Skills

Python development
Data engineering
LLM integration
RAG framework design
Relational databases
Vector databases
PySpark
Kubernetes
Docker
AWS SageMaker
AWS EKS
AWS Amplify
React
Elasticsearch
Git
GitLab
Jira
Confluence
AI governance
Agile

Tools

SageMaker
EKS
Kubernetes
Docker
GitLab
Jira
Confluence
React
AWS Amplify

Job description

Active TS/SCI security clearance required. Benz Technology is unable to sponsor clearances. U.S. citizenship required.

About the Role

You will design, develop, and deploy AI and ML solutions spanning LLM-powered tooling, RAG architectures, and analytics workflows within governed, secure cloud environments. This role covers the full lifecycle from data engineering and model development through production deployment, with direct integration into front-end experiences and automated software analysis systems. You will collaborate closely with customers and engineering teams to build compliant, auditable, and high-impact AI capabilities.

Responsibilities
  • Design and implement LLM-powered applications including RAG frameworks, fine-tuned models, and orchestration pipelines
  • Develop and maintain data engineering workflows using Python, PySpark, and cloud-native tools
  • Deploy and manage containerized AI workloads on Kubernetes and AWS infrastructure including SageMaker and EKS
  • Integrate AI capabilities with static analysis, automated testing, and software certification systems
  • Build front-end analytics experiences using React and AWS Amplify to surface and summarize data for end users
  • Implement and maintain AI/ML governance frameworks ensuring models are compliant, versioned, and auditable
  • Contribute to Agile development cycles using Git, Jira, and Confluence with consistent CI/CD practices
Required Qualifications
  • Python development and data engineering
  • LLM integration, fine-tuning, and RAG framework design
  • Relational and vector database design
  • PySpark and Jupyter Notebooks
  • Kubernetes and Docker
  • AWS SageMaker and AWS EKS
  • AWS Amplify and React
  • Elasticsearch Static analysis tooling experience
  • Git, GitLab, Jira, and Confluence
  • AI/ML governance framework implementation
  • Agile development practices
Desired Qualifications
  • Rust programming language
  • AI coding assistants such as Claude Code or OpenAI Codex
  • Hugging Face and model registry tools such as Bailo
  • CI/CD pipeline design and tooling
  • GMLearn or Container Yard platform experience
  • Query Time Analytics Experience
  • Deploying AI systems within secure enclaves or classified networks
  • Familiarity with customer corporate tools and internal data repositories
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