Generative AI Engineer | LLMs, MLOps & Azure

Apex Systems; Cleveland, Northern (OH, KY); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401K with company match

ESPP

Job summary

Apex Systems in the United States is seeking a lead ML/Generative AI Engineer to design, develop, and deploy advanced AI solutions using Azure OpenAI, LangChain, and MLOps best practices. You will collaborate across teams to integrate Azure, Snowflake, and other technologies into scalable AI applications.

The role emphasizes prompt engineering, RAG, model observability, and secure CI/CD pipelines; 4–6 years in ML/MLOps, plus 1+ year generative AI.

Qualifications

• Bachelor’s in Computer Science or 7+ years of software development 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

Python

LangChain

Hugging Face

PyTorch

RAG

Prompt engineering

Education

Bachelor’s in Computer Science

Tools

Azure OpenAI

Snowflake Cortex

Snowpark

Docker

Kubernetes

GitHub Actions

MLflow

Job description

Apex Systems in the United States is seeking a lead ML/Generative AI Engineer to design, develop, and deploy advanced AI solutions using Azure OpenAI, LangChain, and MLOps best practices. You will collaborate across teams to integrate Azure, Snowflake, and other technologies into scalable AI applications.

The role emphasizes prompt engineering, RAG, model observability, and secure CI/CD pipelines; 4–6 years in ML/MLOps, plus 1+ year generative AI.