Machine Learning Engineer at Auror, using data science to reduce retail crime through innovative ML systems. Collaborate with product teams and develop impactful solutions leveraging real - time data.
About the role
- Senior Machine Learning Engineer focusing on AI solutions development and deployment for Liminal. Collaborating with cross-functional teams to build scalable AI-driven software that drives efficiency and innovation.
Responsibilities
- Collaborate with cross-functional teams to define and design AI solutions aligned with business objectives
- Build, deploy, and maintain production-grade machine learning models and systems that drive efficiency and innovation across multiple departments
- Ensure AI solutions meet performance, scalability, and reliability standards in a production environment
- Continuously improve machine learning models through fine-tuning, retraining, and incorporating new data and feedback
- Monitor and optimize the performance of deployed models and systems, implementing updates and resolving issues proactively
- Work with the AI Solutions Architect to develop pipelines for automated retraining, testing, and deployment of AI models
- Leverage cloud platforms (e.g., AWS, Azure, Google Cloud) for model deployment, orchestration, and monitoring
- Implement and manage robust infrastructure to support AI applications, ensuring scalability and fault tolerance
- Develop and maintain data pipelines for efficient data ingestion, preprocessing, and feature engineering
- Conduct exploratory analyses to identify opportunities for AI innovation
- Design and prototype solutions, validating feasibility through proof of concept (PoC) projects
Requirements
- 3+ years of experience in ML/AI development including production-environment deployments
- Bachelor's degree or higher in a relevant field
- Proficiency in Python and its machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
- Hands-on experience with data pipeline development, including data ingestion, preprocessing, and feature engineering
- Experience with large language models (LLMs) and natural language processing (NLP), including prompt engineering and fine-tuning techniques
- Knowledge of advanced methods such as Retrieval-Augmented Generation (RAG) and platforms like OpenAI or Hugging Face
- Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud)
- Excellent problem-solving skills and ability to troubleshoot and optimize complex AI systems
- Strong communication skills to work effectively with technical and non-technical stakeholders.
Benefits
- Health insurance
- Paid time off
- Professional development
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