AI Engineer

Job Title: AI Engineer


Location: Pennington, NJ


Full Time

Job Description


Must Have Technical/Functional Skills

Description:

Summary The EET EEDS Cognitive Implementation Engineer is responsible for designing, developing, and implementing intelligent employee experiences that leverage conversational AI, Large Language Models (LLMs), agentic workflows, and enterprise integrations. The role combines traditional deterministic workflow development with emerging AI capabilities to deliver scalable, secure, and effective employee solutions. The ideal candidate can design and develop solutions that solve complex employee needs through a combination of structured business processes, AI powered reasoning, and intelligent automation. These experiences may leverage APIs, databases, enterprise systems, knowledge repositories, retrieval frameworks, and AI agents that dynamically orchestrate tools and information. Top candidates possess a strong understanding of both software engineering and AI system design, allowing them to determine when a deterministic workflow is appropriate versus when an agentic approach can deliver greater value. They can connect multiple technologies including virtual assistants, orchestration platforms, LLMs, enterprise APIs, and AI agents into seamless user experiences. Key Responsibilities Collaborate with business partners, architects, engineers, and platform teams to define technical requirements and ensure optimal solution design. Design, develop, and deploy intelligent employee experiences leveraging conversational AI, virtual assistants, and generative AI capabilities. Build deterministic workflows, business process automations, and reusable orchestration components that can be leveraged across multiple domains. Design and implement agentic solutions that leverage Large Language Models, retrieval systems, tool calling, and autonomous task execution. Develop and maintain prompt frameworks, prompt libraries, and orchestration patterns to enable scalable AI capabilities. Integrate AI solutions with enterprise systems through APIs, databases, messaging frameworks, and knowledge repositories. Design and support Model Context Protocol (MCP) integrations that enable secure access to enterprise tools, data sources, and services. Evaluate business use cases and determine the most appropriate architecture leveraging deterministic workflows, LLM orchestration, AI agents, or hybrid approaches. Perform testing, validation, observability, and troubleshooting for both traditional workflow solutions and AI based systems. Monitor, tune, and optimize AI solutions to improve quality, reliability, performance, and user experience. Ensure solutions adhere to enterprise AI governance, security, compliance, and risk management standards.