Software Engineer - AI Product Integration

Intapp•Charlotte, NC

4d•Hybrid

About The Position

Intapp is seeking a Software Development Engineer to lead the design, build, and AI-powered enhancement of our rapidly growing suite of enterprise cloud applications. In this hybrid engineering and product role, you will drive the integration of AI technologies — including large language models, agentic systems, and retrieval-augmented workflows — across multiple products. Each application is designed to fill a specific business need and includes a web-based user interface, back-end business layer, database, and other modules such as web services, integration components, and mobile apps. As a Software Development Engineer – AI Product Integration, you’ll operate at the intersection of engineering and product: building robust software while shaping AI-driven features that deliver real user value. Your responsibilities will span hands-on development, AI feature design, cross-functional product collaboration, code review, performance and scalability, and mentoring. The ideal candidate combines deep technical expertise with strong product intuition and a passion for leveraging AI to solve complex business problems.

Requirements

Proficiency in C#
Knowledge of Model Context Protocol (MCP) for connecting tools and data into models
Experience in developing enterprise-grade web services
Experience working with cloud platforms such as Azure, AWS, or GCP
Practical experience integrating large language models (LLMs) into production software, including prompt design, context management, and responsible AI considerations
Ability to collaborate with product managers and designers to translate business requirements into AI-augmented product features, balancing technical feasibility with user value

Nice To Haves

Hands-on experience with LLM and RAG engineering, including retrieval design, chunking, vector stores, and structured outputs, using tools such as OpenAI, LangChain or LlamaIndex.
Experience in building and orchestrating agentic systems that call tools and reason over retrieved context, using frameworks such as LangGraph or AutoGen, and OpenAI tool calling or the Responses API.
Familiarity with LLM evaluation and observability tools such as LangSmith or Langfuse
Experience with guardrails and security measures such as prompt injection defense and output validation

Responsibilities

Lead hands-on development of products and AI-powered features, from initial prototype through production-grade delivery
Partner with Product Management to define and drive AI integration strategy across multiple products, shaping features from concept through maturity
Communicate and partner with engineers, product management, data scientists, DevOps, and other key team members and stakeholders to align on AI capabilities, product direction, and delivery timelines.
Define and contribute to AI feature technical requirements, specifications, prompt strategies, model selection trade-offs, implementation decisions, and documentation
Perform code review and provide constructive feedback with regard to code quality, scalability, performance, and responsible AI practices including safety, reliability, and cost efficiency
Mentor less experienced team members on software design, performance, scalability, and AI engineering best practices including LLM integration patterns and agentic system design
Design and implement end-to-end AI workflows — including prompt engineering, retrieval pipelines, tool-calling agents, and output validation — that integrate seamlessly into enterprise product surfaces
Evaluate and monitor AI feature performance in production using observability tools, driving continuous improvement through experimentation and data-driven iteration