Cognitive Implementation Engineer - Onsite

USDC•Charlotte, NC

8h•Onsite

About The Position

Genesis10 is seeking a Cognitive Implementation Engineer for an onsite contract opportunity with a Global Financial Institution. This role involves designing, developing, and implementing intelligent employee experiences using conversational AI, Large Language Models (LLMs), agentic workflows, and enterprise integrations. The engineer will combine traditional workflow development with AI capabilities to create scalable, secure, and effective employee solutions. The ideal candidate will design and develop solutions for complex employee needs by integrating structured business processes, AI-powered reasoning, and intelligent automation to create seamless user experiences.

Requirements

Strong understanding of both software engineering and AI system design.
Experience with programming languages such as Java or Python.
Ability to connect multiple technologies including virtual assistants, orchestration platforms, LLMs, enterprise APIs, and AI agents into seamless user experiences.
Proficiency with Microsoft Office Suite.

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.