About Louisa AI
Louisa AI, a cutting-edge institutional revenue enablement platform, was originally developed at Goldman Sachs and became an independent entity in 2023. It utilizes AI to maximize revenues by mapping the expertise and relationships within organizations, primarily serving financial institutions. Louisa AI emphasizes connecting people through AI, not replacing them, leveraging relationship graphs and news integration to enhance revenue generation and connections based on expertise, relationships, and relevant information.
Responsibilities:
As an ML Ops engineer on the Louisa team, you will have the opportunity to:
Model Deployment:
- Implement and automate the deployment of machine learning models into production environments.
- Collaborate with data scientists and software engineers to package models for seamless integration.
Infrastructure Management:
- Set up and manage infrastructure for machine learning workloads, leveraging cloud services and containerization technologies.
- Optimize and scale infrastructure to accommodate evolving machine learning needs.
Continuous Integration and Continuous Deployment (CI/CD):
- Establish and maintain CI/CD pipelines for automating the testing, integration, and deployment of machine learning models.
- Ensure version control and reproducibility of machine learning experiments.
Monitoring and Logging:
- Implement robust monitoring and logging solutions to track the performance of deployed models.
- Set up alerts and triggers to detect anomalies and ensure timely response to issues.
Scalability and Efficiency:
- Optimize and automate resource allocation for machine learning workloads, considering factors such as cost, performance, and scalability.
- Work on improving the efficiency of model training and inference processes.
Security and Compliance:
- Implement security measures to protect machine learning systems and data.
- Ensure compliance with data protection regulations and company policies.
Minimum Qualifications:
- Bachelor’s degree or relevant work experience in Computer Science, Mathematics, Electrical Engineering or related technical discipline.
- Minimum 5 years of relevant development experience.
- Proficiency in cloud platforms (e.g., AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes).
- Experience with CI/CD tools and practices.
- Strong scripting and automation skills (e.g., Python, Bash).
- Familiarity with Natural Language Processing (NLP) and other data science algorithms.
- Experience with MLOps frameworks like MLflow.
- Knowledge of security best practices in machine learning environments.
- Excellent problem-solving and analytical skills and strong communication skills.
Extra awesome:
- Exposure to deep learning approaches and modeling frameworks (Transformers, PyTorch, Tensorflow, Keras, etc.)
- Experience developing and maintaining ML systems built with open source tools.
- Exposure to big data technologies like Hadoop, spark, hive.
- Comfortable multi-tasking, managing multiple stakeholders and working in a global team.
