Generative AI Engineer: LLMs, MLOps & Azure

Apex Systems; Cleveland (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

Apex Systems is seeking an experienced ML Engineer to design, develop, and deploy advanced ML and generative AI solutions using LLMs, prompt engineering, and MLOps best practices. You will integrate Azure, Snowflake, and related technologies into scalable AI applications.

Work with cross-functional teams to ship business-driven AI solutions, fine-tune models, build CI/CD pipelines, and implement model observability, while staying current with the latest AI advancements.

Qualifications

• Bachelor’s degree in Computer Science or equivalent experience.
• 4–6 years in ML/MLOps/software development, including 1+ year in generative AI.
• Proficiency in Python, LangChain, Hugging Face, PyTorch.
• Experience with Azure (OpenAI, ML, DevOps) and Snowflake (Snowpark, ML, Cortex).
• Strong understanding of RAG, vector databases, and prompt engineering.
• Familiarity with MLOps tools (MLflow, Docker, Kubernetes, GitHub Actions).

Responsibilities

• Build generative AI systems using Azure OpenAI, Cognitive Search, and Snowflake Cortex.
• Fine-tune LLMs/SLMs using techniques like RLHF and prompt tuning.
• Develop CI/CD pipelines and implement model observability (e.g., hallucination detection).
• Collaborate with cross-functional teams to deliver business-driven AI solutions.
• Maintain documentation and stay current with AI advancements.

Skills

RAG

vector databases

prompt engineering

MLOps

Education

Bachelor’s in Computer Science

Tools

Python

LangChain

Hugging Face

PyTorch

Azure

Snowflake

MLflow

Docker

Kubernetes

GitHub Actions

Job description

Apex Systems is seeking an experienced ML Engineer to design, develop, and deploy advanced ML and generative AI solutions using LLMs, prompt engineering, and MLOps best practices. You will integrate Azure, Snowflake, and related technologies into scalable AI applications.

Work with cross-functional teams to ship business-driven AI solutions, fine-tune models, build CI/CD pipelines, and implement model observability, while staying current with the latest AI advancements.