Enterprise Solutions AI Applied Engineer

Overview

Hands-on engineer who builds and deploys practical AI solutions that improve internal workflows and employee productivity, implementing agentic and Generative AI features grounded in enterprise knowledge and data.

What you'll do

• Build and enhance AI-powered features (e.g., copilots, summarization, classification, routing, Q&A) aligned to defined business workflows.
• Partner with business users to clarify requirements, run demos, capture feedback, and iterate toward measurable outcomes.
• Support pilot launches, troubleshoot issues, and contribute to smooth adoption through user training and clear documentation.
• Develop agents grounded in the company's internal knowledge base, using Retrieval-Augmented Generation (RAG) patterns and retrieval quality improvements.
• Design conversational flows for internal functions (e.g., HR helpdesk), including intent handling, escalation paths, and handoff to human support when needed.
• Implement simple agentic or predictive models for operational use cases (e.g., predicting customer support ticket volume) under guidance from senior engineers.
• Manage the feature backlog for an AI product area: write user stories, define acceptance criteria, and coordinate UAT.
• Contribute to evaluation and regression testing using approved checklists; help monitor quality, latency, and cost for deployed features.
• Create and maintain documentation (how-to guides, runbooks, and user enablement materials) to support long-term sustainability.

What you'll need

• 3+ years of experience in software engineering or applied solutions development (or equivalent practical experience).
• Proficiency in Python and API-based integration.
• Working knowledge of SQL and data access patterns.
• Working familiarity with large language models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG).
• Exposure to agent frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, or similar) and the ability to implement tool/function calling patterns.
• Basic understanding of enterprise security, privacy, and data governance requirements; ability to follow established guardrails and escalate risks early.
• Strong communication skills and the ability to collaborate effectively with technical and business stakeholders.
• Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).