LLM / GenAI Engineer

Evlo AI; Miami (FL); On-site; USD 120,000 - 180,000; Full time

Job summary

Evlo AI in Miami is seeking a software engineer to build production-grade AI systems. You will design robust RAG pipelines, agentic workflows, and fine-tuning pipelines, collaborating with applied scientists and backend engineers.

You will implement scalable Python-based solutions, optimize embeddings, and contribute to evaluation frameworks for LLMs in production environments.

Qualifications

• 3–6 years of software engineering experience, with at least 2 years building LLM apps in production.
• Deep familiarity with LLM orchestration frameworks and prompt engineering best practices.
• Strong proficiency in Python and REST API design, plus cloud infra integration.
• Solid understanding of embeddings, vector spaces, tokenization, and model quantization.
• Bonus: experience with CUDA kernels, vLLM or TGI deployments, and open-source contributions.

Responsibilities

• Design and implement production RAG pipelines.
• Build and optimize vector DB integrations for low-latency search.
• Develop LLM evaluation frameworks including benchmarks and regression tests.
• Execute parameter-efficient fine-tuning pipelines (LoRA/QLoRA).
• Write observable, tested Python code and participate in reviews.

Skills

Python

async programming

REST API design

cloud infrastructure

LLM orchestration

prompt engineering

context window optimization

embedding models

vector spaces

tokenization

model quantization

CUDA kernels

vLLM

TGI

open-source contributions

Job description

About The Role

The role is for someone who has moved beyond basic prompting and understands what it takes to build production-grade AI systems: robust RAG pipelines, agentic workflows, fine-tuning pipelines, and systematic evaluation frameworks.

About The Role

The role is for someone who has moved beyond basic prompting and understands what it takes to build production-grade AI systems: robust RAG pipelines, agentic workflows, fine-tuning pipelines, and systematic evaluation frameworks.

The engineering team owns complex pieces of a high-scale AI platform, working directly with applied scientists and backend engineers to deploy performant LLM applications.

Key Responsibilities

• Design and implement production RAG pipelines using LangChain, LlamaIndex, or custom Python architectures
• Build and optimize vector database integrations such as Pinecone, Weaviate, or pgvector for low-latency semantic search
• Develop systematic LLM evaluation frameworks including benchmark suites, LLM-as-judge pipelines, and automated regression testing
• Execute parameter-efficient fine-tuning pipelines using LoRA and QLoRA on specialized domain datasets
• Write observable, tested, and well-documented Python code; participate actively in architecture reviews and deployment pipelines

What We Are Looking For

• 3 to 6 years of software engineering experience, with at least 2 years specifically focused on building LLM applications in production
• Deep familiarity with LLM orchestration frameworks, prompt engineering best practices, and context window optimization
• Strong proficiency in Python, async programming, REST API design, and cloud infrastructure integration
• Solid understanding of embedding models, vector spaces, tokenization, and model quantization techniques
• Bonus: Experience with custom CUDA kernels, open-source model deployment via vLLM or TGI, and published contributions to AI open-source projects