LLM / GenAI Engineer

Latitude; Chicago, Northern (IL, KY); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Latitude in Chicago is seeking a Senior AI Platform Engineer who has moved beyond basic prompting to build production-grade AI systems. You will own complex components of the core AI platform and collaborate with applied scientists, backend engineers, and enterprise stakeholders to deliver scalable generative AI applications.

You will design end-to-end RAG pipelines, optimize vector database integrations, and develop evaluation frameworks, while writing robust Python code and participating in

Qualifications

• 3–6 years of software engineering experience with 2+ years in LLMs and production systems.
• Hands-on experience with retrieval-augmented generation and fine-tuning.
• Experience with AI tooling and model deployment in cloud environments.

Responsibilities

• Design and implement end-to-end RAG pipelines and retrieval architectures.
• Build and optimize vector DB integrations for production-scale search.
• Develop LLM evaluation frameworks and automated regression tests.
• Conduct instruction fine-tuning and parameter-efficient fine-tuning (LoRA/QLoRA) on domain data.
• Write clean, observable Python code and participate in architecture reviews.

Skills

Python development

Asynchronous programming

RESTful API design

Cloud infrastructure

LLM orchestration (LangChain/LlamaIdx)

Vector databases

Distributed systems

Benchmarking/evaluation

Tools

LangChain

LlamaIndex

Pinecone

Weaviate

pgvector

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.

The position will own complex pieces of the core AI platform and work directly with applied scientists, backend engineers, and enterprise stakeholders to deliver scalable generative AI applications.

Key Responsibilities

• Design and implement end-to-end RAG pipelines using LangChain, LlamaIndex, or custom retrieval architectures


• Build and optimize vector database integrations including Pinecone, Weaviate, or pgvector for semantic search at production scale


• Develop systematic LLM evaluation frameworks using benchmark suites, LLM-as-judge pipelines, and automated regression testing


• Execute instruction fine-tuning and parameter-efficient fine-tuning techniques such as LoRA and QLoRA on domain-specific datasets


• Write observable, tested, and well-documented Python code while participating actively in architecture and code reviews

What We Are Looking For

• 3 to 6 years of software engineering experience, with a minimum of 2 years specifically focused on LLMs and generative AI in production


• Deep familiarity with LLM orchestration frameworks like LangChain or LlamaIndex and hands-on experience with major foundation model APIs and open-source models


• Solid understanding of embedding models, vector databases, and semantic similarity optimization in distributed environments


• Strong Python development skills with proven comfort in asynchronous programming, RESTful API design, and cloud infrastructure


• Bonus: Experience publishing open-source AI tools, contributions to ML research, or background in distributed systems performance tuning