AI App Lead

Confiz is seeking an AI App Lead in Denver, CO. As the AI Lead you own both the architecture and the hands-on delivery of AI-powered applications built on Databricks, Python, and React. You set technical direction for a team of data, ML, and full-stack engineers while still writing production code yourself.

You will design LLM, RAG, and agentic systems on the Databricks Lakehouse, expose them through Python services, and deliver them to users through React front ends. You build secure, scalable, cost-efficient production solutions, and you are accountable for making sure the code matches the architecture.

• Leads the end-to-end architecture of AI solutions, including LLM applications, RAG pipelines, and agentic workflows, from data ingestion to the user interface.
• Designs and builds data and ML pipelines on Databricks using Delta Lake, Unity Catalog, MLflow, Databricks Workflows, and Mosaic AI (Model Serving, Vector Search, Agent Framework).
• Builds and reviews production Python services (FastAPI or similar) that expose models, retrieval, and agents through secure, well-documented REST APIs.
• Architects and contributes to React (Type Script) front ends for AI experiences, such as chat interfaces, streaming responses, source citations, and human-in-the-loop review screens.
• Selects LLM providers and models (e.g., Databricks-hosted foundation models, Azure OpenAI, Anthropic) and retrieval options based on quality, latency, cost, and data-governance fit.
• Establishes LLMOps and MLOps practices: model and prompt versioning, automated evaluation, tracing, monitoring, drift detection, and retraining.
• Produces architecture and design artifacts for complex AI applications and is accountable for ensuring the delivered code meets them.
• Leads code and architecture reviews, and mentors engineers on RAG, agent design, prompt engineering, and evaluation.
• Ensures solutions meet security, privacy, and compliance standards, including Unity Catalog access controls, PII handling, guardrails, audit trails, and responsible AI practices.
• Identifies and automates fixes for recurring issues to improve the operational stability and cost efficiency of AI workloads.
• Translates business requirements into technical architecture in partnership with product owners and stakeholders, clearly communicating trade-offs to both technical and non-technical audiences.

• 10+ years of software engineering experience, including 3+ years designing and delivering AI/ML systems in production and hands-on work with generative AI.
• Demonstrated experience leading engineers and setting technical direction, while remaining hands-on in code.
• Advanced Python: production services, async programming, testing, packaging, and PySpark.
• Hands-on Databricks Lakehouse experience:
  Delta Lake, Unity Catalog, MLflow, Databricks Workflows, and Spark-based processing; experience with Model Serving and Vector Search.
• Strong React with Type Script: component architecture, state management, API integration, and streaming or real-time UIs.
• Deep understanding of LLMs, RAG architectures, agentic systems, prompt engineering, LLM evaluation, and model fine-tuning.
• Proven experience designing scalable distributed systems, REST APIs, and microservices, with a solid grasp of system design and security principles.
• Strong SQL, data modeling, ETL/ELT pipeline design, and data governance.
• Experience with Azure, AWS, or GCP AI and data services.
• Hands-on infrastructure-as-code experience (Terraform, Bicep, or Cloud Formation), including Databricks Asset Bundles (DABs) for versioning and deploying jobs, pipelines, and model serving endpoints through CI/CD.
• Proficient in CI/CD, Git, automated testing, and the full software development life cycle.
• Strong stakeholder communication: able to translate architecture into business value for technical and non-technical audiences.

• Databricks certifications, such as Generative AI Engineer Associate, Machine Learning Professional, or Data Engineer Professional.
• Experience with agent and orchestration frameworks (Lang Graph, Lang Chain, Llama Index, DSPy) and the Model Context Protocol (MCP).
• Experience with vector databases beyond Databricks Vector Search, such as Pinecone, Weaviate, or pgvector.
• Exposure to Docker and Kubernetes.
• Experience with Next.js, design systems, and accessible UI development.
• Prior experience in client-facing, pre-sales, or solutioning capacity.
• Experience in a regulated industry such as financial services.

We have a global team of…