Agentic AI Developer III

Realpage · Serilingampalli

  • Experience6–7 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Posted24 Sept 2026
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About Realpage

Realpage is hiring in Serilingampalli in real estate construction. This role looks for around 6+ years of experience.

Skills

  • Python
  • TypeScript
  • JavaScript
  • SQL
  • OpenAI Agents SDK
  • Temporal
  • GraphQL
  • MCP
  • REST APIs
  • React
  • GCP
  • Kubernetes
  • GKE
  • AWS
  • ECS
  • EC2
  • Docker
  • CI/CD
  • ELK
  • OpenAI tracing

The role

A generative AI engineer at a property technology company builds agentic AI applications with Python, OpenAI Agents SDK, and Temporal workflows, integrating RAG pipelines and production services across backend and frontend systems. The role also applies React and GraphQL to deliver reliable, evaluated AI features.

Full job description

Overview

RealPage is leading the Generative AI transformation within the PropTech industry. Our Agentic AI team builds next-generation AI applications and enhances existing products with advanced Generative AI capabilities.

As a Senior AI Engineer (Dev III), you are first and foremost a strong software engineer who has moved into agentic AI. You design, build, and ship production-grade AI features end to end — owning everything from backend services and data pipelines to LLM orchestration and UI integration. You work independently, drive solutions to completion, and think in terms of systems rather than scripts.

Responsibilities

Software & Systems Engineering (the foundation)

Write clean, production-grade Python (primary), TypeScript/JavaScript or any similar language code with tests, CI/CD, and observability. Own features end to end: API design, data modeling, service implementation, deployment, and monitoring. Design systems that are reliable, debuggable, and maintainable — not just demos. Participate in system design and architecture discussions; make and defend trade-off decisions.

Agentic AI Engineering

Build agentic workflows with OpenAI Agents SDK (preferred) and durable, long-running orchestration via Temporal. Design agents where MCP, tool use, and RAG pipelines are central; integrate vector stores, embeddings, and retrieval based on project needs. Approach agent design evals-first — define what "good" looks like, then build and iterate against measurable evals rather than vibes. Ship AI features such as conversational assistants, summarization, OCR pipelines, and copilots, using whichever LLM/multimodal provider best fits the problem.

Evals & Quality

Treat evals as a first-class part of building agents — design eval datasets, metrics, and harnesses alongside the feature, not after. Use evals to drive iteration: measure, diagnose failures, fix, re-measure; gate regressions and reduce hallucinations.

Full-Stack Delivery

Frontend: React. Backend: GraphQL, MCP, and REST APIs. Strong with SQL for analytics — query and shape data for retrieval, evaluation, and reporting.

Collaboration & Ownership

Work independently, drive solutions, and unblock yourself — a systems thinker who designs and architects. Collaborate with AI engineers, software engineers, PMs, and designers; communicate clearly to technical and non-technical audiences.

Qualifications

6+ years in Software Engineering (or Data Science with strong engineering), with 1–2 years hands-on building with LLMs / agentic AI. Strong software engineering fundamentals — able to own features end to end and think like a systems architect. Languages: Python (primary), SQL (good for analytics), TypeScript/JavaScript. Agentic stack: OpenAI Agents SDK (preferred), Temporal workflows; LangSmith for building/eval (good to have). Backend / Frontend: GraphQL, MCP, REST APIs; React. Cloud: GCP (Kubernetes / GKE) and AWS (ECS, EC2); Docker and CI/CD. Observability: ELK, LangSmith, OpenAI tracing. Evals-first mindset when building agents.

Nice to Have

RAG / ML knowledge and MLOps. Voice / realtime agents: VAD, latency optimization, STT/TTS.