ML Engineer

Job Description:

Our client, top Systems Integrator, is hiring key resources to support the AI/ML modernization and transformation of the Postal Services systems and applications. In this role, you will play a critical part in designing, developing, and deploying Machine Learning models and intelligent automation solutions supporting nationwide postal operations. Huge opportunity to apply advanced AI capabilities to one of the largest retail and logistics environments in the U.S.!

Requirements:

• 4+ years of experience developing & deploying ML models and data-driven automation solutions – building use cases from requirements all the way through model production deployment
• Including ML model solution design, development, testing, UAT, and go-live in a cloud environment (AWS or Azure)
• Extensive experience with Python and/or React for ML model development
• Hands-on experience building and deploying agentic AI systems using Enterprise LLM platforms such as Microsoft Copilot or Anthropic Claude
• Experience with RAG (retrieval-augmented generation) pipeline design and implementation
• Experience supporting a Government or Federal agency/client
• Must be able to obtain Public Trust clearance – government-level background check w/ fingerprinting required prior to start date

Nice to have:

• Active Public Trust clearance and/or experience working for the Government

• CI/CD pipeline familiarity (GitHub, GitLab, Azure DevOps)
• Azure or AWS Data certifications

Responsibilities**:**

• Build ML use cases end to end — gather requirements from stakeholders, design the solution, write production-quality code, and deploy into the live postal environment
• Build and maintain agentic AI systems and RAG pipelines for information extraction and retrieval
• Integrate AI capabilities into existing business applications and processes
• Adapt across a wide range of use cases spanning operations — these engineers are injected into existing dev teams and must be self-sufficient
• Optimize and monitor deployed AI models to ensure sustained performance
• Collaborate directly with business stakeholders and technical teams to define and deliver solutions