6 Month Contract - High Likelihood of Extension
• *THIS ROLE IS HYBRID AND BASED IN NYC - URGENT NEED SO RELOCATION REQUIREMENT WILL NOT BE CONSIDERED**
Role Responsibilities:
• Develop and implement MLOps pipelines to automate the end-to-end machine learning workflow.
• Design and maintain infrastructure as code, ensuring scalability and reliability of data processing.
• Build event tables for effective data ingestion and storage, focusing on vector databases.
• Create and manage code for data ingestion, including automated versioning using GitHub Actions.
• Collaborate with data scientists to integrate LLM frameworks and enhance machine learning models.
• Monitor and capture errors during processes, ensuring timely notifications and resolutions via email alerts.
• Optimize existing data pipelines and frameworks for efficiency and performance.
• Work closely with cross-functional teams to ensure seamless integration of ML systems.
Qualifications:
• Proven experience in data engineering and machine learning engineering, with a focus on MLOps practices.
• Strong understanding of large language models (LLMs) and relevant frameworks, such as LlamaIndex.
• Proficiency in cloud platforms, particularly AWS and Azure.
• Experience with infrastructure as code tools and methodologies.
• Familiarity with automated deployment processes and CI/CD practices.
• Ability to work in a fast-paced environment and adapt to evolving technologies.
• Hands-on experience in coding and problem-solving in a practical engineering role, preferably outside of corporate settings.
Ideal Candidate:
• A "unicorn" candidate who blends expertise in data engineering and ML engineering with a touch of data science.
• Self-motivated and innovative, with a strong desire to industrialize machine learning processes.
Location: New York, NY
Posted: Oct. 14, 2024, 8:31 a.m.
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