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Generative AI Engineer

OmegaHires
4 days ago
Contract
On-site
Phoenix, Arizona, United States
$53 - $57 USD hourly
Generative AI

Job Title: Generative AI Engineer
Location: Phoenix, AZ (Onsite/Hybrid)
Duration: 12 Months

Job Summary

We are seeking a highly skilled Generative AI Engineer with deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI systems. The ideal candidate will have a strong foundation in Python development and proven experience in designing and deploying scalable, production-grade AI solutions. This role requires strong analytical thinking, system design capabilities, and a passion for building intelligent, next-generation AI applications.

Key Responsibilities

  • Design, develop, and deploy scalable generative AI solutions using LLMs
  • Build and optimize RAG pipelines leveraging vector databases and semantic search techniques
  • Develop and implement agentic AI systems using modern frameworks
  • Fine-tune prompts and optimize model performance for accuracy and efficiency
  • Integrate AI models into production environments via APIs and microservices
  • Collaborate with cross-functional teams including data engineers, architects, and product stakeholders
  • Ensure robustness, scalability, and reliability of AI systems in production
  • Stay current with emerging trends and advancements in generative AI technologies

Required Qualifications

  • 6+ years of experience in software development with a focus on AI/ML
  • Strong proficiency in Python for AI/ML development
  • Hands-on experience with LLMs (OpenAI, Anthropic, or open-source models)
  • Experience building RAG pipelines using vector databases (e.g., FAISS, Pinecone, Chroma)
  • Experience with agentic frameworks such as LangChain, LangGraph, or AutoGen
  • Strong understanding of prompt engineering, embeddings, and semantic search
  • Proven experience designing scalable, production-ready AI systems
  • Excellent problem-solving and system design skills

Preferred Qualifications

  • Experience with cloud platforms such as AWS, Azure, or GCP
  • Familiarity with MLOps practices and model lifecycle management
  • Knowledge of data engineering pipelines and distributed systems
  • Experience building and consuming REST APIs and microservices
  • Understanding of AI governance, security, and compliance frameworks