GenAI Engineer: Deploy Scalable LLM Solutions

Infosys; Dallas (TX); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401(k) plan

Paid holidays + PTO

Job summary

Infosys is seeking a Data Science Consultant 2 to join Infosys Topaz, an AI-first suite that accelerates business value through generative AI. You will develop data prep tasks, build models, and deploy scalable analytics solutions using Python, Lang Chain, and LLMs within Azure OpenAI or AWS Bedrock environments.

You will collaborate with cross-functional teams, apply governance and best practices, and contribute to training and thought leadership while delivering production-ready, data-driven

Qualifications

• Bachelor's degree or foreign equivalent required.
• Experience building enterprise GenAI applications with orchestration frameworks.
• Proficiency with LLM platforms and enterprise APIs/databases.
• Production APIs and microservices with CI/CD and testing.

Responsibilities

• Develop data preparation tasks and model deployment for scalable solutions.
• Build predictive models and data-driven analyses to address business challenges.
• Apply governance, observability, and cost controls to models.
• Collaborate with product/platform teams and stakeholders to refine requirements.
• Produce reusable code, templates, and knowledge for the team.

Skills

Python

Lang Chain

Lang Graph

LLama Index

LLMs orchestration

Azure OpenAI

AWS Bedrock

OpenAI Platforms

FastAPI

Docker

Kubernetes

Git

MLOps

Education

Bachelor's degree or foreign equivalent

Tools

FastAPI

Docker

Kubernetes

Git

Lang Chain tooling

Job description

Infosys is seeking a Data Science Consultant 2 to join Infosys Topaz, an AI-first suite that accelerates business value through generative AI. You will develop data prep tasks, build models, and deploy scalable analytics solutions using Python, Lang Chain, and LLMs within Azure OpenAI or AWS Bedrock environments.

You will collaborate with cross-functional teams, apply governance and best practices, and contribute to training and thought leadership while delivering production-ready, data-driven