Demo

LLM Engineer Dallas TX WFO, 3 days hybrid

ICS Global Soft, Inc.
Dallas, TX Full Time
POSTED ON 4/17/2025
AVAILABLE BEFORE 6/17/2025

Job Details

Job Mode/location:Dallas TX WFO, 3 days hybrid

1 level of internal vetting Bar raiser or Vetsmith , 2 internal interviews and 1 customer

Required skills

  • 5 years of professional experience in building Machine Learning models & systems
  • 1 years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly prompt engineering, RAG, and agents.
  • Experience in driving the engineering team toward a technical roadmap.
  • Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
  • Understanding of Cloud services, including Azure, Google Cloud Platform, or AWS
  • Excellent communication skills to effectively collaborate with business SMEs


Roles & Responsibilities

  • Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
  • Codebase ownership: Maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
  • Cloud integration: Aide in deployment of GenAI applications on cloud platforms (Azure, Google Cloud Platform, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
  • Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.
  • Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
  • Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.


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