Top Tech Jobs & Startup Jobs in Denver & Boulder, CO

Designs and prototypes scalable agentic AI architectures across Azure, AWS, or GCP. Responsibilities include multi-agent orchestration, LLM applications, RAG pipelines, memory and vector search, enterprise integrations, cloud-native engineering, code reviews, AI governance, security guardrails, and stakeholder leadership. The role requires hands-on development, framework evaluation, and translating business needs into production-ready AI platforms.

Deploys, integrates, and scales enterprise Generative AI solutions across Azure environments. The role develops LangGraph agentic workflows, fine-tunes and evaluates LLMs, reduces hallucinations, integrates retrieval systems and APIs, and collaborates with customer engineering teams. Responsibilities also include requirements documentation, process mapping, automation engineering, software architecture, and communicating business value.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

The Databricks Architect will design and implement large-scale data solutions, optimizing performance and reliability, while leading initiatives in data engineering.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Technology Partner will lead technology solutions and data transformation initiatives for Fortune 100 clients, providing thought leadership and managing executive relationships.

Lead design and delivery of Snowflake-centric cloud data platforms. Engage with clients to gather requirements, architect scalable secure solutions, provide technical leadership, develop roadmaps, advise executives, and guide implementation best practices across the analytics stack.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

The Databricks Architect will design and implement data architecture solutions using Databricks, AWS, and Snowflake, focusing on MLOps and data governance.