Agentic AI & Palantir Enginer

Enterprise Engineering Inc. is seeking an experienced Agentic AI Engineer or Consultant to design, develop, test, deploy, and enhance enterprise AI solutions. The role involves building reusable AI agent capabilities, integrating enterprise data and systems, developing solutions with Palantir Foundry and AIP, and collaborating across technical and business teams.

Responsibilities:

• Design, build, test, and deploy Agentic AI solutions addressing enterprise business use cases
• Participate across the complete AI solution lifecycle, including ideation, experimentation, prototyping, development, testing, deployment, and production support
• Build reusable agentic skills, tools, and capabilities that can be composed across multiple agents and business use cases
• Design agent workflows involving reasoning, orchestration, tool use, enterprise data, APIs, and human-in-the-loop interactions
• Rapidly prototype new AI capabilities and transition successful experiments into scalable, production-ready solutions
• Develop appropriate testing, evaluation, monitoring, and governance approaches for production Agentic AI applications
• Develop data and AI solutions using Palantir Foundry and Palantir AIP
• Build and manage Foundry data pipelines, ontology components, and application capabilities
• Design enterprise semantic and context layers using Palantir's ontology capabilities
• Develop AI and LLM-powered workflows using AIP Logic, AIP Automate, and AIP Assist
• Deploy and operationalize solutions within the Palantir environment
• Where applicable, serve as a Palantir subject matter resource within the delivery pod and help coach and upskill other team members
• Develop Agentic AI solutions using Azure AI Foundry, Databricks, and related cloud AI services
• Work with Databricks Genie and other emerging agentic and conversational data capabilities
• Build and maintain Git repositories, branching strategies, automated testing, and CI/CD pipelines supporting production AI deployments
• Engineer and integrate structured and unstructured enterprise data for AI applications
• Design semantic layers, enterprise ontologies, knowledge/context layers, or context spines that provide agents with reliable business context
• Integrate enterprise systems, APIs, databases, vector stores, search capabilities, and other tools with AI agents
• Work with models from OpenAI, Anthropic, and other leading LLM providers
• Evaluate models based on quality, latency, security, scalability, deployment architecture, and cost
• Implement appropriate approaches for LLM evaluation, observability, guardrails, security, and cost optimization