LLM Engineer

Description

LLM Engineer

Fetcherr experts in deep learning, algo-trading, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.

We are seeking a talented LLM engineer to help us advance our team.

The ideal candidate should be a self-driven, motivated, and independent thinker who is passionate about using data and machine learning to drive business outcomes.

Responsibilities:

• Develop and implement cutting-edge LLM models and algorithms for demand forecasting applications.
• Conduct research and experimentation to identify and evaluate new approaches for improving model accuracy and performance.
• Collaborate with cross-functional teams, including business stakeholders, data engineers, and software developers, to deploy and maintain machine learning systems in production.
• Communicate technical findings and insights to non-technical stakeholders, including executives and other decision-makers.

Requirements

Must have:

• 3+ years of hands-on experience in LLM.
• 3+ years of hands-on experience in data science / machine learning.
• Strong coding skills in Python and SQL, with experience using related open-source libraries and frameworks, including TensorFlow/PyTorch and Pandas.
• Experience building tabular machine learning models using gradient boosting methods and deep learning
• Strong understanding of machine learning systems in production, including good coding practices for testing, reproducibility, and version control.
• Excellent written and verbal communication skills.

Nice to have:

• Degree in Computer Science, Statistics, or related quantitative field.
• Published papers, patents, or professional posts.
• Experience in leveraging deep learning and machine learning in domains such as finance/trading, reinforcement learning, or natural language processing.
• Experience with MLOps and cloud platforms like GCP.
• Experience with workflow orchestration tools like Apache Airflow or Dagster to schedule and monitor machine learning workflows.
• Strong data visualization and data analysis skills.
• Knowledge of code optimization, cloud computing, containerization, and continuous integration/continuous deployment (CI/CD) pipelines.
• Competitive programming and data science (Kaggle like) exp.