Job Description

3 month contract with extensions

Pay range falls from $65-75/hr

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.

• 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.

• 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.

• 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.

• 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.

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