Senior ML/AI Engineer

AdtalentincNew York City, NY

About The Position

A fast-paced, innovation-driven startup is seeking a highly skilled and experienced Senior ML/AI Engineer with deep expertise in Large Language Models (LLMs). The ideal candidate is a software engineer proficient in Python, machine learning, and data systems, with hands-on, production-level experience using LLMs (such as Bedrock, LangGraph, etc.) within the past two years. This role requires a strong balance between immediate product delivery and future scalability.

Requirements

  • M.S. or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field
  • Proven software engineering skills with strong proficiency in Python, machine learning, and data infrastructure
  • Demonstrated production-level experience with LLMs (e.g., Bedrock, LangGraph, LLaMA) within the last 2 years
  • Deep knowledge of AWS services, especially AWS Bedrock
  • Experience with AI/ML frameworks like TensorFlow or PyTorch
  • Hands-on experience with prompt engineering and model optimization
  • Prior startup experience is strongly preferred
  • Strong problem-solving skills and ability to work independently and collaboratively
  • Excellent communication skills, especially when translating complex technical ideas to non-technical stakeholders

Nice To Haves

  • Experience with predictive modeling and optimization techniques
  • Familiarity with backend systems in startup environments
  • Understanding of MLOps tools and workflows
  • Contributions to open-source AI projects

Responsibilities

  • Design, build, and maintain AI pipelines on AWS for LLaMA and other LLMs
  • Leverage AWS Bedrock to develop scalable AI applications
  • Develop and manage knowledge-based systems to enhance model outputs
  • Train, fine-tune, and optimize LLMs for diverse use cases
  • Apply prompt engineering techniques to improve model performance and UX
  • Collaborate with product and engineering teams to integrate AI solutions
  • Stay current with AI/ML research and incorporate relevant advancements
  • Diagnose and resolve model performance and pipeline issues
  • Document workflows, experiments, and key learnings

Benefits

  • Access to educational support for professional and technical development
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