ML Engineer

• Develop and maintain scalable, production-ready infrastructure for Large Language Models (LLMs): This includes working with both open-source and proprietary tools to ensure efficient and reliable deployment of LLMs in a high-volume environment.
• Optimize LLM performance: Analyze and optimize LLM performance through techniques such as model distillation, quantization, and efficient data structures, ensuring optimal efficiency and resource utilization.
• Stay updated on the latest advancements in machine learning and data classification techniques.
• Collaborate with data scientists and engineers to ensure seamless integration of models into existing infrastructure or create a new one.
• Perform code reviews and provide technical guidance to other team members.
• Contribute to the development and improvement of data science tools and frameworks used by the team.
• Contribute to open-source projects: Actively engage with the open-source community by contributing code, documentation, and expertise to relevant LLM projects, fostering innovation and knowledge sharing.

• 5+ years of experience in a backend or ML engineering role with a demonstrable track record of successfully deploying code in production environments.
• Experience with big data technologies such as Snowflake, Spark, Hadoop, or similar, for processing and analyzing large datasets.
• Hands-on experience with MLOps tools such as Airflow, DVC, MLflow, BentoML, Lammaindex, ZenML, or similar tools for managing the machine learning lifecycle.
• Passion for building custom tools from scratch to solve our unique challenges and improve ML workflows.
• Strong problem-solving skills and ability to take ownership of tasks from conception to full implementation, working both independently and collaboratively within a team.
• Knowledge of large language models (LLMs) and their applications is a plus.
• In-depth understanding of public cloud platforms (GCP/AWS/Azure) and their respective application architectures and technologies is a plus.

Mploy