Senior Generative AI Engineer — Production AI & MLOps

TEKsystems; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) plan

Life insurance

Disability insurance

Paid time off

Job summary

TEKsystems is seeking a Senior AI/ML Engineer specializing in Generative AI to design, build, deploy, and maintain scalable AI solutions across client programs. You will work at the intersection of software engineering, data, and cloud delivery, delivering secure and maintainable AI-enabled applications.

The role requires strong Python coding, hands-on experience with cloud-native AI services, and practical delivery using LLMs, embedding, and MLOps.

Qualifications

• Typically 6+ years of professional software engineering, data engineering, machine learning engineering, or AI engineering experience.
• Strong Python and AI/ML fundamentals are required.
• Experience with AI/ML, Generative AI, LLM integration, RAG, MLOps, or production AI delivery is preferred.

Responsibilities

• Design, build, deploy, and maintain production-grade AI/ML and Generative AI solutions across client and internal initiatives.
• Mentor junior and mid-level engineers and contribute to architectural decisions.
• Collaborate with product owners, data engineers, DevOps, and client teams to translate requirements into AI-enabled solutions.

Skills

Generative AI

Python

Cloud platforms

MLOps

LLM integration

Retrieval augmented generation

Vector search

Prompt engineering

Observability

AI governance

Tools

FastAPI

PyTorch

TensorFlow

Hugging Face

Job description

TEKsystems is seeking a Senior AI/ML Engineer specializing in Generative AI to design, build, deploy, and maintain scalable AI solutions across client programs. You will work at the intersection of software engineering, data, and cloud delivery, delivering secure and maintainable AI-enabled applications.

The role requires strong Python coding, hands-on experience with cloud-native AI services, and practical delivery using LLMs, embedding, and MLOps.