Machine Learning Engineer

GenAI/Machine Learning Engineer 3 month contract with extensions Pay range falls from $65-75/hr Job Description An Insight Global customer is seeking a GenAI / Machine Learning Engineer to design, build, and scale AI-powered solutions that transform how unstructured data is processed and accessed. This role will focus on developing LLM-based workflows, embedding services, and retrieval-augmented generation (RAG) pipelines to enhance semantic search and user engagement. The ideal candidate thrives in collaborative environments and enjoys working across engineering, product, and data teams to operationalize cutting-edge GenAI technologies. Responsibilities: • Design and implement ingestion and transformation workflows for unstructured content (e.g., PDFs, reports, emails) with semantic tagging, chunking, and metadata enrichment to support GenAI and search applications. • Develop and manage embedding services leveraging LLMs (Anthropic, Meta, Mistral, etc.) and vector databases such as OpenSearch, FAISS, AWS Kendra, or Azure AI Search. • Build and manage RAG (Retrieval-Augmented Generation) pipelines using AWS Bedrock models, Knowledge Bases, and agents. • Create interactive user interfaces (UIs) for AI applications using React or Streamlit. • Develop CI/CD and GitOps automation workflows (Terraform, GitHub Actions, ArgoCD) for deploying scalable applications. • Collaborate with cross-functional teams to establish frameworks and best practices that support future AI and ML initiatives. Must Haves • 3+ years of hands-on experience with GenAI or ML infrastructure. • Experience designing and building embedding services, vector databases (e.g., FAISS, Weaviate, OpenSearch), and RAG architectures. • Proven experience processing and transforming unstructured data (PDFs, reports, knowledge bases) into AI-ready formats, including semantic chunking and metadata enrichment. • Hands-on experience integrating hosted LLM services (AWS Bedrock, Azure AI Foundry, etc.) into production-ready applications. • Track record of building and operationalizing GenAI solutions, including prompt engineering or model fine-tuning (LoRA, PEFT). • Strong background in CI/CD, GitOps workflows, and infrastructure-as-code (Terraform, ArgoCD). • Experience deploying on AWS EKS or other Kubernetes environments. Nice to Haves • Experience building front-end tools using React or Streamlit for AI model interaction. • Familiarity with enterprise search solutions or knowledge graph integration. • Exposure to financial services, data security, or regulated industries. • Experience with multi-cloud environments or large-scale data ingestion systems. Soft Skills / Culture Fit • Highly self-sufficient with minimal need for hand-holding. • Strong communicator and active listener. • Collaborative team player with a problem-solving mindset. • Must not rely on AI tools to complete interviews or assessments