AI Platform Engineer — Cloud-Native APIs & LLM Workflows

HeartCentrix Solutions; Dallas (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

HeartCentrix Solutions seeks an AI Platform Software Engineer to build core services powering enterprise AI applications. You will develop scalable Python-based APIs and microservices for LLM workflows and AI agent orchestration.

Collaborate with platform, ML, and software teams to deploy production AI solutions using Docker, Kubernetes, and CI/CD pipelines, while writing automated tests and ensuring production reliability.

Qualifications

• 5+ years of software engineering experience.
• Experience with Java, Go, or TypeScript.
• Experience building REST or gRPC APIs and microservices.
• Hands-on experience with AWS or Azure, Docker, Kubernetes, CI/CD, and Infrastructure as Code (Terraform, CloudFormation, or Pulumi).
• Experience with LLM integrations, prompt engineering, or AI/ML application development.
• Familiarity with LangChain, LangGraph, or similar AI orchestration frameworks.

Responsibilities

• Build and maintain Python-based APIs and microservices for AI applications.
• Develop services supporting LLM integration, prompt management, and AI agent orchestration.
• Build reliable, scalable cloud-native applications using Docker, Kubernetes, and CI/CD pipelines.
• Collaborate with software, platform, and ML engineers to deploy production AI solutions.
• Write automated tests and support production systems through monitoring and continuous improvement.

Skills

Python

REST APIs

Go/Java/TypeScript

Docker/Kubernetes

CI/CD

AWS/Azure

Infrastructure as Code

LLM integration

LangChain/LangGraph

Tools

Terraform

CloudFormation

Pulumi

Job description

HeartCentrix Solutions seeks an AI Platform Software Engineer to build core services powering enterprise AI applications. You will develop scalable Python-based APIs and microservices for LLM workflows and AI agent orchestration.

Collaborate with platform, ML, and software teams to deploy production AI solutions using Docker, Kubernetes, and CI/CD pipelines, while writing automated tests and ensuring production reliability.