Generative AI Engineer

GenAI/ML Engineer Denver, CO Short Term Contract, 2 to 3 days in office No 3rd Parties please or Referrals Key Responsibilities: • Design and implement ingestion and transformation workflows for unstructured content (e.g., PDFs, reports, emails), including chunking, semantic tagging, and metadata enrichment. • Build and manage vector embedding services using leading LLMs (e.g., Anthropic, Meta, Mistral), and integrate with vector databases such as OpenSearch, FAISS, AWS Kendra, or Azure AI Search. • Develop and operationalize RAG pipelines using AWS Bedrock, incorporating Knowledge Bases and GenAI agents. • Create interactive UIs using React or Streamlit to support end-user exploration and validation of GenAI results. • Implement infrastructure automation and deployment pipelines using Terraform, GitHub Actions, and ArgoCD, supporting GitOps practices across environments. • Prototype and scale GenAI solutions in partnership with product and data teams, helping define the architectural blueprint for intelligent applications. • Establish reusable frameworks and automation patterns that accelerate AI adoption and integration into business systems. Required Qualifications: • 3+ years of hands-on experience in GenAI or ML infrastructure, including: • Designing and managing vector databases (e.g., FAISS, Weaviate, OpenSearch) • Implementing RAG architectures • Strong experience transforming unstructured data into AI-consumable formats, including: • Semantic chunking • Metadata enrichment • Content classification • Practical experience integrating hosted LLMs (e.g., via AWS Bedrock, Azure AI Foundry) into production pipelines. • Proven success in building GenAI solutions involving: • Prompt engineering • Adapter-based fine-tuning (e.g., LoRA, PEFT) • Deep familiarity with CI/CD and GitOps practices: • GitHub Actions, ArgoCD • Infrastructure-as-Code using Terraform • Kubernetes deployments, particularly on AWS EKS Preferred Qualifications: • Experience deploying LLM-based systems in regulated or enterprise environments. • Familiarity with multi-modal GenAI workflows (text, images, documents). • Background in MLOps, ML governance, or AI observability frameworks. • Contributions to open-source GenAI or MLOps tooling.