LLM engineer

Sails Software•Saint Louis, MO

About The Position

As an LLM Engineer at Sails Software Inc, you will design, develop, and optimize large language models that support innovative client solutions and enhance company products and services. You will take ownership of projects from conception through deployment, collaborating with data scientists, software engineers, and product managers to integrate models into existing systems and applications. The role includes selecting appropriate model architectures, training and fine-tuning models, conducting experiments, evaluating results, and monitoring and optimizing model performance after deployment. You will also document processes and share knowledge with colleagues. The team works collaboratively in an agile environment and values open communication, diverse perspectives, initiative, and ownership.

Requirements

  • Strong understanding of natural language processing and machine learning concepts, including large language models.
  • Proficiency in programming languages such as Python and Java; the role also calls for familiarity with programming languages and machine learning frameworks.
  • Experience with TensorFlow, PyTorch, or similar machine learning frameworks.
  • Familiarity with model deployment tools and techniques.
  • Ability to analyze data and evaluate model performance metrics.
  • Strong problem-solving skills and critical thinking about complex issues.
  • Proven ability to work collaboratively in a team-oriented environment.
  • Excellent communication skills to articulate ideas and collaborate effectively.
  • Proactive mindset and a strong sense of ownership and accountability.

Responsibilities

  • Research and select appropriate model architectures.
  • Design, train, and fine-tune large language models to address business challenges.
  • Integrate models into existing systems and applications in collaboration with software developers and product teams.
  • Conduct experiments, analyze results, and evaluate performance metrics to optimize models.
  • Monitor and maintain model performance after deployment.
  • Adapt model implementations to changing business requirements and user needs.
  • Document processes and share knowledge with team members.
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