Applied AI Engineer

Overview

On Site
Depends on Experience
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

LLM

Job Details

Job Title: Applied AI Engineer

Houston, TX hybrid 3 days per week onsite

Job Summary:

We are seeking a talented and driven Applied AI Engineer to join our growing Data & AI team. This role is ideal for an experienced software engineer with a deep understanding of generative AI technologies and a passion for building scalable, production-grade AI solutions. You will be responsible for designing, prototyping, and deploying GenAI-powered applications, while also contributing to our AI platform architecture across cloud environments such as Google Cloud Platform (Google Cloud Platform) and Microsoft Azure.

Key Responsibilities:

  • Translate business needs into robust, scalable GenAI technical solutions.
  • Design, prototype, and implement LLM-driven applications using techniques such as RAG, prompt engineering, fine-tuning, and vector search.
  • Develop APIs and reusable software components in Python to support GenAI applications.
  • Leverage orchestration frameworks (e.g., LangChain, LlamaIndex, or LangGraph) to deliver dynamic and modular AI workflows.
  • Collaborate with cross-functional teams to integrate AI capabilities into business applications.
  • Deploy and monitor GenAI models and pipelines using cloud-native tools, Kubernetes, and serverless architectures.
  • Continuously evaluate model and system performance, implementing improvements as needed.
  • Create and maintain technical documentation and support materials for deployed solutions.

Minimum Requirements:

  • Bachelor s or Master s degree in Computer Science, AI/ML, or a related field.
  • 5+ years of software development experience with strong Python skills.
  • 2 3+ years of hands-on experience building GenAI/LLM-based applications.
  • Experience developing multi-step agent workflows using LangGraph or similar orchestration frameworks.
  • Proficient in designing retrieval pipelines: document loaders, chunking strategies, embedding models, and vector database integration.
  • Strong grasp of GenAI concepts, including:
    • Retrieval-Augmented Generation (RAG)
    • Embeddings & vector databases (e.g., FAISS, Pinecone, ChromaDB)
    • Prompt engineering and fine-tuning
    • LLM APIs (e.g., OpenAI, Claude, Gemini)
  • Experience deploying cloud-native solutions using Google Cloud Platform and/or Azure.
  • Solid understanding of API design, microservices, and software architecture patterns.
  • Familiarity with version control systems (e.g., Git, Azure DevOps).
  • Experience with Docker and Kubernetes.
  • Demonstrated ability to build and scale AI/ML solutions from proof-of-concept to production.

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