Internal AI Solution Development

Design and build internal AI solutions that support engineering productivity and software delivery, including:

AI-powered developer workflows and assistants

Agentic SDLC automation patterns

Internal tools for code analysis, documentation, testing, migration, and engineering support

Reusable prompt, tool-calling, and workflow patterns Reference implementations that can be adopted by engineering teams

Develop solutions that are practical, scalable, maintainable, and aligned with RealPage engineering standards. Agentic Workflow and Platform Enablement

Build reusable capabilities that help teams adopt AI consistently across the organization, including:

Multi-step agentic workflows

Tool-calling and orchestration patterns

RAG-based internal knowledge solutions

Shared SDKs, templates, and integration examples

Reusable components for copilots, agents, and AI-enabled engineering workflows

Partner with senior architects and engineering leaders to establish patterns that can scale beyond one team or use case. Engineering Team Enablement

Work directly with engineering teams, champions, and internal stakeholders to help them adopt AI effectively. Responsibilities include:

Pairing with teams on AI use cases and implementation patterns

Providing technical guidance on LLM, RAG, and agentic workflow design

Supporting proof-of-concept efforts and helping mature them into repeatable practices

Creating playbooks, examples, templates, and documentation for internal engineering use

Participating in office hours, workshops, and AI enablement sessions AI Evaluation, Quality, and Responsible Use





Help define and apply practical evaluation and governance practices for internal AI solutions, including:

Prompt and workflow evaluation

Accuracy, relevance, and usefulness testing

Safety and responsible AI considerations

PII and sensitive-data handling

Logging, observability, and feedback loops

Human-in-the-loop review patterns where appropriate

Ensure internal AI solutions are developed with quality, security, privacy, and reliability in mind. Delivery and Cross-Functional Collaboration

Partner with engineering leadership, product teams, architecture, security, and other stakeholders to identify and deliver high-impact AI use cases. Responsibilities include:

Translating engineering productivity needs into AI-enabled solutions

Supporting roadmap-aligned internal AI initiatives

Contributing to adoption and capacity-improvement goals

Helping measure the impact of AI enablement efforts

Communicating technical concepts clearly to engineering and non-engineering audiences Performance, Reliability, and Cost Awareness

Design AI solutions with practical performance and cost considerations, including:

Model selection and routing

Prompt and context optimization

Caching and retrieval efficiency

Latency and reliability considerations

Build-vs-buy recommendations Avoidance of vendor lock-in where practical

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Engineer / Architect (Ahmedabad)
🏢 IndiaNIC
📍 Ahmedabad