Senior AI Engineer

ORION•Omaha, NE

5d•$140,935 - $226,905•Hybrid

About The Position

Senior AI Engineer – Applied LLMs/ Generative AI At Orion, we’re building the intelligent infrastructure that powers modern financial advisory. As part of our mission to unify planning, investing, and client engagement, we’re looking for a Senior AI Engineer to rapidly deploy cutting-edge large language models (LLMs) and generative AI into production, powering scalable systems and customer experiences. This is a hands-on, product-facing role focused on shipping working AI features. You’ll work closely with product managers, designers, and engineers to build systems that directly impact thousands of advisors and millions of investor accounts. Technical Skills Proven experience working with large language models (e.g. OpenAI GPT, Claude, LLaMa) Familiarity with embedding models, understanding tokenization, prompt engineering and fine-tuning (e.g., LoRA). Practical experience with at least 1 vector database: Pinecone, Weaviate, FAISS Strong proficiency with orchestration tools like LangChain, Haystack or LlamaIndex for building RAG systems Ability to design and implement retrieval-augmented generation (RAG) systems. Experience in data preprocessing, chunking and vectorization pipelines

Requirements

5+ years in software engineering, with 2+ years in applied ML or AI
Deep understanding of LLMs, embeddings, RAG architecture, and vector search
Strong grasp of prompt design, fine-tuning strategies, and model evaluation
Proficiency with tools such as: LangChain, LlamaIndex, Pinecone, Weaviate OpenAI, Hugging Face, FastAPI, Docker
Strong engineering discipline and communication skills, especially in cross-functional settings

Nice To Haves

Experience with agent-like systems or intelligent workflows
Background in NLP, recommendations, or personalization
Worked in fast-paced startups or product-led teams

Responsibilities

Integrate LLMs and generative AI into advisor facing products and workflows
Design and build RAG systems using internal and external data sources
Apply techniques like prompt engineering, fine-tuning (e.g. LoRA), and custom embeddings to optimize domain-specific performance
Evaluate and productionize open-source and proprietary models (GPT-4, Claude, LLaMA, Mistral, etc.)
Develop APIs and services to deliver AI- powered features at scale
Collaborate across product and engineering teams to deliver rapidly and reliably
Continuously measure AI feature quality: accuracy, latency, and user impact