AI/ML Engineer
New
Washington, DC or Fully RemoteFull-TimeSenior
Salary130,000 - 170,000 USD per year
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Job Details
- Experience
- 5-8+ years
- Required Skills
- DockerPythonPyTorchLLMLangChain
Requirements
- Bachelor degree in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, Applied Mathematics, or related discipline.
- 5-8+ years of professional experience developing production AI or machine learning applications.
- Strong Python programming experience.
- Experience with PyTorch.
- Experience deploying LLMs in production environments.
- Experience with LangGraph, LangChain, CrewAI, Semantic Kernel, or similar orchestration frameworks.
- Experience implementing Retrieval-Augmented Generation (RAG).
- Experience with vector databases and semantic search.
- Experience deploying AI models on edge or resource-constrained devices.
- Experience with model optimization techniques including quantization, model compression, or inference acceleration.
- Experience designing evaluation frameworks for AI systems.
- Experience with Docker and cloud-native AI deployment.
Responsibilities
- Design and implement AI capabilities supporting intelligent data characterization, classification, prioritization, and decision support.
- Evaluate, optimize, and deploy open-weight foundation models appropriate for resource-constrained edge environments.
- Develop efficient inference pipelines supporting heterogeneous compute environments ranging from embedded processors to workstation-class systems.
- Implement Retrieval-Augmented Generation (RAG), semantic search, and knowledge retrieval capabilities where appropriate.
- Design AI orchestration workflows supporting distributed inference across multiple edge devices.
- Develop evaluation methodologies for AI accuracy, latency, resource utilization, and operational performance.
- Implement model monitoring, observability, testing, and automated evaluation frameworks.
- Collaborate with software engineers to integrate AI models into production software platforms.
- Optimize models using quantization, pruning, distillation deployment technologies.
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