AI Engineer

Insight Global is looking to hire a REMOTE AI Engineer for a growing healthcare organization focused on advancing its enterprise AI initiatives. This individual will take a lead role in designing, developing, and deploying AI-powered applications that support operational, administrative, and clinical use cases. They will work closely with executive leadership and business stakeholders to implement agentic AI solutions that have already been identified within the organization and help accelerate the company's AI journey. Responsibilities include building and deploying LLM-powered applications, RAG architectures, copilots, automation assistants, and AI agents while leveraging Azure Cloud, Azure OpenAI, Databricks, Azure Data Factory, SQL, and Python. This person will integrate AI solutions into enterprise systems, develop scalable data pipelines, evaluate foundation models, and provide technical leadership on AI architecture and best practices. The ideal candidate is a seasoned AI engineer who has successfully delivered production-grade generative AI solutions and can guide the organization through the next phase of AI adoption. Compensation starts at $95/hour depending on experience. Must Haves • 6+ years of experience building enterprise-grade data and AI solutions using Azure, Databricks, SQL, and Python. • Hands-on experience designing and deploying Generative AI applications, including LLMs, RAG pipelines, AI agents, chatbots, or copilots in production environments. • Strong experience with Azure cloud services, Azure OpenAI, and Databricks for AI and data platform development. • Experience building data pipelines, API integrations, and scalable AI services using tools such as LangChain, LlamaIndex, FastAPI, or similar frameworks. • Experience leading AI initiatives and serving as a technical SME for architecture, development, and deployment of AI solutions. Plusses • Azure or Databricks certifications. • Experience working with healthcare data, including EHR, FHIR, or HL7. • Experience with vector databases such as Pinecone, FAISS, Chroma, or Weaviate. • Knowledge of HIPAA compliance and PHI security requirements. • Experience with MLOps tools such as MLflow, Kubeflow, or Airflow.