Senior AI/LLM Optimization Engineer

Data Capital Inc. is 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

• 7+ years in software/ML engineering, including 2+ 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.

Preferred: vLLM, TensorRT-LLM, model fine-tuning, AI guardrails, financial services experience.

Screening Requirement: Applicants must describe one production LLM optimization project, the techniques implemented, and measurable results.