GenAI ML Engineer — AWS, FastAPI & Bedrock

Aimlroles; Plano (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

Tiger Analytics is seeking an experienced Machine Learning Engineer with Gen AI experience to design and deploy scalable AI-powered systems. You will build high-performance APIs and microservices on AWS Fargate, integrating LLMs with providers like OpenAI and AWS Bedrock.

The role emphasizes CI/CD, MLOps, and collaboration with ML and DevOps teams to improve latency, throughput, and backend efficiency in production environments.

Qualifications

• 5+ years of hands-on software development experience with Python.
• Proven expertise in FastAPI and microservice architecture.
• Strong understanding of cloud-native applications, container orchestration (ECS, Docker), and AWS tools.
• Proficiency in LLM API integration and working with Generative AI frameworks.
• Experience implementing CI/CD, IaC, and ML pipelines across AWS environments.

Responsibilities

• Design, build, and optimize high-performance APIs and microservices using Python (FastAPI) deployed on AWS Fargate (ECS).
• Integrate LLM and Generative AI APIs using providers such as AWS Bedrock, OpenAI, and others.
• Collaborate with ML and DevOps teams to design CI/CD and MLOps pipelines within the AWS ecosystem.
• Contribute to architectural decisions around scalability, latency management, and backend efficiency for AI-powered systems.
• (Preferred) Leverage familiarity with Bedrock Agent Core services to integrate intelligent agent capabilities.
• Develop and maintain JSON RESTful APIs, adhering to OpenAI API conventions and best practices.

Skills

Python

FastAPI

Microservices

AWS

LLM API integration

GenAI

Docker

Tools

AWS Fargate (ECS)

Bedrock

OpenAI

CI/CD

Job description

Tiger Analytics is seeking an experienced Machine Learning Engineer with Gen AI experience to design and deploy scalable AI-powered systems. You will build high-performance APIs and microservices on AWS Fargate, integrating LLMs with providers like OpenAI and AWS Bedrock.

The role emphasizes CI/CD, MLOps, and collaboration with ML and DevOps teams to improve latency, throughput, and backend efficiency in production environments.