LLM / GenAI Engineer

About The Role
The LLM / GenAI Engineer will design, build, and operate production AI systems spanning retrieval-augmented generation, agentic workflows, model adaptation, and evaluation. The work goes beyond prompt engineering: it requires reliable data pipelines, measurable model behavior, scalable inference, and clear operational controls.
Based in Denver, CO with a remote working model, the role will partner with applied scientists, product engineers, and platform teams to turn emerging language-model capabilities into dependable user-facing products. Success means improving answer quality, latency, cost, and safety while maintaining systems that can be monitored and iterated in production.
Key Responsibilities
Design and implement RAG and agentic application architectures using Python, LangChain, LlamaIndex, or custom orchestration services
Build ingestion, chunking, embedding, retrieval, reranking, and citation pipelines using vector technologies such as Pinecone, Weaviate, Elasticsearch, or pgvector
Develop evaluation frameworks with curated benchmark sets, LLM-as-judge workflows, human review processes, regression testing, and quality dashboards
Fine-tune and adapt foundation models using supervised fine-tuning, LoRA, QLoRA, prompt optimization, and domain-specific training datasets
Deploy and optimize model-serving systems on AWS, GCP, or Azure, balancing throughput, latency, context-window limits, and inference cost
Instrument production AI services with tracing, logging, feedback capture, and monitoring for hallucinations, drift, safety issues, and performance regressions
Collaborate on technical design reviews, write tested and maintainable software, and document model behavior, data lineage, and operational runbooks
What We Are Looking For
3–8 years of software engineering, machine learning engineering, or applied AI experience, including at least 1 year delivering LLM or GenAI systems to production
Strong Python skills with experience building REST or gRPC services, asynchronous workflows, automated tests, and production data-processing pipelines
Hands-on experience with LLM application patterns including RAG, tool calling, structured generation, prompt versioning, and multi-step agent workflows
Working knowledge of transformer architectures, tokenization, embeddings, attention mechanisms, fine-tuning strategies, and model evaluation methods
Experience with cloud infrastructure and deployment tooling such as Docker, Kubernetes, CI/CD, managed model APIs, and at least one major cloud platform
Bachelor’s or master’s degree in computer science, machine learning, data science, engineering, or a related technical field, or equivalent practical experience
Bonus: Experience with open-source models such as Llama, Mistral, or Qwen; distributed training; GPU optimization; guardrails and red-teaming; vector search tuning; or observability tools such as LangSmith, OpenTelemetry, or Weights & Biases

Work arrangement
Yes