AI-ML Architect Sr

Company



Munich Re America Services



Location



Princeton
, United States

Key Responsibilities:

• As an AI, GenAI, ML Architect, you will be responsible for defining the architectural strategy for GenAI and ML projects; Create workflows, identify toolsets, and ensure scalability.

• You will work closely with data scientists, data engineers, developers, and business leaders to align technical implementation with business goals.

• As an AI, GenAI, ML Architect you will be responsible to establish standardized end-to-end processes for AI/ML operations, including experimentation, staging, production, and monitoring.

• You will play a key role in managing risks associated with AI and ML deployment, ensuring ethical implementation and continuous improvement.

• Develop and implement Machine Learning models, focusing on training & deployment process optimization, including parallelization.

• Design and build cloud-based data pipelines, integrating ML models into existing software solutions. Write and maintain robust, scalable production-quality code for deployment of ML models and services.

• Create and deploy inference endpoints (APIs) and optimize compute architectures and data structures.

• Implement logging and metric generation for models, ensuring comprehensive monitoring and addressing model degradation.

• Lead the deployment of machine learning models in Azure cloud environments, managing the full lifecycle from development to production. Build CI/CD pipelines for machine learning models using Azure tools to streamline deployment and updates.

• AI Governance: Ensure compliance with AI governance policies and ethical guidelines, including data privacy, fairness, and transparency in AI systems Work closely with data scientists, engineers, and product managers to review code and integrate ML models into products and services.

• Collaborate with cross-functional teams to continuously improve and advance technologies and methods for ML systems. Keep up with the latest advancements in machine learning and related technologies to continuously improve model performance.

• Develop deep relationships with key customer stakeholders and IT decision makers

• Be a Voice of Customer to share insights and best practices, connect with business and platform teams to remove key blockers.

Qualifications:

• Strong proficiency in Python and relevant scripting languages, with experience in software development and scripting for Machine Learning. Expertise with ML libraries and frameworks (e.g., Pandas, Numpy, Scikit-Learn, TensorFlow, PyTorch, Databricks, MLFlow, dvc, dbt) and the ability to select the right tools for the use case.

• Proven experience in optimizing ML training processes, including parallelization techniques to improve model performance. Experience building inference endpoints (APIs) and managing compute architecture for efficient model inference and data handling.

• Skilled in implementing ML monitoring systems, including logging and metric generation for machine learning models. Very good Azure and data & AI technology skills - specifically: Databricks / Spark, Azure Datalake Store, Azure AI Search, Azure ML, Dataiku.

• Skilled in cloud platforms (Azure, AWS) for deploying machine learning models and managing model lifecycle, with a focus on addressing model degradation. Experience with CI/CD pipelines for ML, using tools such as Azure Pipelines,or similar.

• Proficiency with data science tools and best practices for ensuring high-quality and efficient ML workflows. Several years of experience in machine learning, data science, or a related field, with a strong understanding of statistics and data analysis. Extra credit for cloud deployment experience (Azure), containerization (Docker), vector search engines (Azure AI Search), knowledge graphs, ML publications, or competition participation.

Preferred Qualifications:

• Advanced Degree: Master's degree in Computer Science, Engineering, Mathematics, with 5+ years of ML implementation experience or Ph.D. with 2+ years of hands-on ML Project experience.

• Experience with Big Data: Strong proficiency with big data technologies such as Azure Databricks and Spark.

• Leadership Experience: Previous experience leading a team of data scientists or engineers.