Need Data Engineer with GenAI

Hi,
Title: Data Engineer GenAI Focus (RAG, Agents, LLMs) Location: Phoenix, AZ (Onsite/Hybrid) No remote

Duration: 12+ Months

Contract Type: W2 Only

We are looking for a Data Engineer with strong Generative AI experience who has evolved from traditional data engineering into building cutting-edge AI-powered solutions. This role is ideal for someone who has hands-on experience with RAG pipelines, agent-based systems, and custom LLM applications in production environments.
Key Responsibilities:

• Design and build scalable data pipelines to support AI/ML and GenAI workloads

• Develop and deploy RAG (Retrieval-Augmented Generation) architectures using vector databases

• Build and manage LLM-powered applications, including custom GPTs and enterprise AI assistants

• Implement agentic workflows using frameworks like Lang

Chain, Auto

Gen, or similar

• Integrate structured and unstructured data sources for AI consumption

• Optimize data models and pipelines for performance, scalability, and reliability

• Collaborate with data scientists, ML engineers, and business stakeholders to deliver AI-driven solutions

• Ensure data quality, governance, and security across AI pipelines

• Work with cloud platforms (AWS/Azure/Google Cloud Platform) for scalable GenAI deployments
  Required Skills:

• 7+ years of Data Engineering experience (ETL, data pipelines, big data processing)

• Proven hands-on experience in Generative AI / LLM-based solutions

• Strong experience building RAG pipelines with vector databases (Pinecone, FAISS, Weaviate, etc.)

• Experience with agent frameworks (Lang

Chain, Lang

Graph, Auto

Gen, CrewAI, etc.)

• Solid programming skills in Python

• Experience working with LLMs (OpenAI, Azure OpenAI, Claude, etc.)

• Strong SQL and data modeling skills

• Experience with Spark, Databricks, or similar big data technologies

• Hands-on experience with cloud platforms (AWS/Azure/Google Cloud Platform)
  Preferred Qualifications

• Experience building custom GPTs or AI copilots

• Familiarity with prompt engineering, embeddings, and vector search

• Knowledge of ML Ops / LLM Ops practices

• Exposure to real-time or streaming data pipelines

• Strong understanding of data architecture and distributed systems