Senior ML Engineer, Applied Machine Learning

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Requirements:

• Proven experience in developing, optimizing, and deploying ML systems in production environments.
• Strong background in building and managing end-to-end training pipelines for ML models.
• Extensive knowledge and hands-on experience in fine-tuning large language models for specific use cases and optimizing them for targeted outcomes.
• Skilled in ML frameworks such as TensorFlow, PyTorch, or similar tools used in ML model development.
• Proficient in Python with a focus on writing efficient, clean, and maintainable code for ML applications.
• Ability to distill complex ML concepts for both technical and non-technical audiences.
• Bachelor's or Master's degree in Machine Learning, Computer Science, Data Engineering, or a related field.
• A track record of delivering and implementing machine learning solutions that have successfully driven value in real-world applications.

Responsibilities:

• Architect, Build, and Optimize ML Systems:
• Develop and deploy robust ML models that deliver high-impact results for real-world applications.
• Training Pipeline Development:
• Design and implement efficient, scalable pipelines to train and retrain ML models, ensuring they meet business needs.
• Fine-Tuning Large Language Models (LLMs):
• Continuously fine-tune LLMs to align with specific enterprise requirements, enhancing accuracy, relevance, and performance.
• Feedback Systems Design:
• Implement and refine feedback loops to iteratively improve the effectiveness of ML models over time.
• Cross-Functional Collaboration:
• Work closely with product and business teams to understand and translate requirements into ML solutions that provide tangible outcomes.
• Stay Current with ML Advancements:
• Keep up with the latest in ML research and best practices, applying insights to our ML infrastructure to ensure it remains at the cutting edge.
• Mentorship and Knowledge Sharing:
• Guide and mentor junior team members, fostering a culture of continuous improvement and technical growth.
• Technical Communication:
• Clearly and effectively communicate ML methodologies, results, and insights to non-technical stakeholders.