AI/LLM Optimization Engineer

Data Capital Incorporation; Virginia; In loco; USD 180.000 - 240.000; Tempo pieno

Descrizione del lavoro

Data Capital Incorporation is seeking an AI/LLM Optimization Engineer with deep production experience to optimize enterprise AI applications for accuracy, performance, scalability, and reliability.

This hands-on role focuses on real-world systems, not prompts or POCs. You will refine RAG pipelines, embeddings, and LLM evaluation frameworks, deploying scalable solutions in AWS and cloud environments to achieve measurable improvements in production models.

Competenze

• 13+ years in software/ML engineering, including 3+ years of production LLM engineering.

Mansioni

• Optimize enterprise LLM applications, RAG pipelines, and AI agents.
• Improve model accuracy, response quality, throughput, and inference latency.
• Implement advanced RAG techniques, embeddings, hybrid search, and re-ranking.
• Develop automated LLM evaluation frameworks, regression testing, and performance benchmarks.
• Apply fine-tuning (LoRA/QLoRA), quantization, caching, and inference optimization.
• Deploy and optimize scalable LLM solutions in AWS/cloud environments.

Conoscenze

Production LLM engineering

Python programming

ML model optimization

Performance benchmarking

Regression testing

Strumenti

PyTorch

Hugging Face

LangChain/LangGraph or LlamaIndex

RAG pipelines

AWS Bedrock/SageMaker

Vector databases

LangSmith / DeepEval

Descrizione del lavoro

We are seeking a highly experienced AI/LLM Optimization Engineer to optimize production-grade Generative AI applications for accuracy, performance, scalability, and reliability.

This is a hands-on engineering role—not a prompt engineering or POC-focused position.

Key Responsibilities

• Optimize enterprise LLM applications, RAG pipelines, and AI agents.
• Improve model accuracy, response quality, throughput, and inference latency.
• Implement advanced RAG techniques, embeddings, hybrid search, and re-ranking.
• Develop automated LLM evaluation frameworks, regression testing, and performance benchmarks.
• Apply fine-tuning (LoRA/QLoRA), quantization, caching, and inference optimization.
• Deploy and optimize scalable LLM solutions in AWS/cloud environments.

Mandatory Technical Requirements

• 13+ years in software/ML engineering, including 3+ years of production LLM engineering.
• Advanced Python, PyTorch, Hugging Face, and LLM architectures.
• Strong hands-on experience with RAG, vector databases, and LangChain/LangGraph or LlamaIndex.
• Proven LLM evaluation and optimization experience using Ragas, DeepEval, LangSmith, or equivalent.
• Experience with AWS Bedrock/SageMaker or comparable enterprise AI platforms.
• Must demonstrate measurable improvements in LLM accuracy, latency, or throughput in production.