Generative AI Engineer

Summary: Design, build, and deploy generative AI solutions, including LLMs and RAG pipelines, to drive organizational impact. Collaborate with cross-functional teams to optimize model performance, ensure responsible AI practices, and establish MLOps workflows.

Generative AI Engineer - Denver

We are seeking a talented Generative AI Engineer to join our growing technology team in Denver. In this role, you will design, build, and deploy generative AI solutions that transform how our organization works, serves customers, and creates value. You will work at the forefront of large language models, multimodal systems, and AI-driven applications, turning cutting-edge research into reliable, production-ready products.

As a Generative AI Engineer based in Denver, you will collaborate closely with data scientists, software engineers, product managers, and business stakeholders to identify opportunities where generative AI can drive measurable impact. Our team values experimentation, rigorous evaluation, and responsible AI practices, and we are looking for someone who combines technical depth with a strong sense of ownership and curiosity.

This position offers the chance to shape our AI strategy from the ground up, influence architectural decisions, and contribute to a culture that prioritizes innovation, continuous learning, and collaboration. Whether you are fine-tuning foundation models, building retrieval-augmented generation pipelines, or optimizing inference performance, your work will directly influence the quality and success of our products.

### Responsibilities

• Design, develop, and deploy generative AI applications using large language models and other foundation models.
• Build and maintain retrieval-augmented generation (RAG) pipelines, including document ingestion, embedding generation, and vector search integration.
• Fine-tune and evaluate pre-trained models for domain-specific tasks, applying techniques such as parameter-efficient fine-tuning and prompt optimization.
• Develop robust evaluation frameworks to measure model accuracy, hallucination rates, latency, and overall output quality.
• Collaborate with product and business teams to translate requirements into scalable AI-driven features.
• Optimize model inference for performance, cost efficiency, and scalability in cloud environments.
• Implement guardrails, content safety measures, and responsible AI practices to mitigate bias and misuse risks.
• Establish MLOps practices, including model versioning, monitoring, logging, and continuous deployment workflows.
• Stay current with advances in generative AI research and evaluate emerging tools, frameworks, and techniques for adoption.
• Document system architecture, design decisions, and best practices to support team knowledge sharing.
• Mentor junior engineers and contribute to code reviews and technical design discussions.

### Requirements

• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related technical field, or equivalent practical experience.
• 3+ years of experience in software engineering or machine learning, with at least 1–2 years focused on generative AI or large language models.
• Strong proficiency in Python and experience with frameworks such as PyTorch, Hugging Face Transformers, or LangChain.
• Hands-on experience with LLM APIs and open-source models, including prompt engineering and fine-tuning workflows.
• Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and embedding models.
• Experience deploying ML or AI services in cloud environments such as AWS, Azure, or Google Cloud Platform.
• Solid understanding of software engineering fundamentals, including version control, testing, APIs, and containerization with Docker.
• Knowledge of responsible AI principles, including bias detection, data privacy, and safe deployment practices.
• Strong analytical and problem-solving skills with the ability to communicate complex technical concepts to non-technical audiences.
• Excellent collaboration skills and a proactive, ownership-driven mindset.