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Stanley David and Associates Linkedin · Posted 3d ago

Machine Learning Engineer

Sunrise, Florida, United States

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Indexed description

Role :: Machine Learning Engineer

Location :: Sunrise, FL

Type :: Fulltime


Experience Required -8+ Years


(Generative AI & LLMs LangChain / LangGraph ML model)


We are seeking a Machine Learning Engineer to design, build, and deploy Generative AI solutions powered by Large Language Models (LLMs). In this role, you will work on end-to-end GenAI use cases, from model selection to production-ready systems. Key Responsibilities Develop and productionize GenAI applications using LLMs (open-source and closed-source). Design agentic workflows using LangChain and LangGraph.


Must Have Technical/Functional Skills:

We are seeking a Machine Learning Engineer to design, build, and deploy Generative AI solutions powered by Large Language Models (LLMs). In this role, you will work on end-to-end GenAI use cases, from model selection to production-ready systems. Key Responsibilities Develop and productionize GenAI applications using LLMs (open-source and closed-source). Design agentic workflows using LangChain and LangGraph.

  • Implement short-term and long-term memory strategies for LLM-based systems.
  • Optimize prompts, retrieval pipelines, and orchestration logic.
  • Collaborate with product and platform teams to deliver scalable AI solutions.


Required Qualifications

  • Strong experience with LLMs (e.g., OpenAI, Anthropic, Llama, Mistral)
  • Hands-on experience with LangChain and/or LangGraph.
  • Solid understanding of LLM memory architecture and state management.
  • Proficiency in Python and ML engineering best practices.


Nice to Have

  • Experience with GCP services (e.g., Vertex AI, BigQuery, GCS).
  • Experience deploying ML/GenAI systems in production environments.
  • data scientist
  • Can do ML model


Roles & Responsibilities

  • Design, develop, and deploy GenAI applications using LLMs.
  • Build and implement agentic workflows using LangChain/LangGraph.
  • Develop ML models and production-ready AI solutions.
  • Implement and manage LLM memory and state management strategies.
  • Optimize prompts, retrieval pipelines, and orchestration workflows.
  • Collaborate with product and platform teams to deliver scalable AI solutions.
  • Deploy, monitor, and maintain AI/ML systems in production environments.
  • Evaluate and integrate open-source and proprietary LLMs.


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