Hiring For AIML Manager - WFH Opportunity

Responsibilities:

• Plan, schedule, mentor, and lead the execution of projects and activities of the team. Including creating, optimizing, and deploying training and inference Deep Learning (DL) workloads. Build and manage a world-class team of data engineering, MLOps engineers, and machine learning engineers.
• Collaborate with internal customers to align priorities across business units. Understand business objectives and develop models along with the project metrics to track progress.
• Coordinate projects across different geographic locations
• Experience leading multiple software engineering projects
• Strong algorithms and Deep Learning skills or experience. Experience with inference of DL models
• Excellent programming, debugging, performance analysis, and test design skills
• Experience doing performance analysis and tuning
• Guide the team to architect an entire AI Engineering platform that allows for deployment and scalability of machine learning models. Grow and develop a world-class team
• Work with stakeholders on the research and software engineering side of the company to understand how to support their teams
• Build tools and capabilities that help with data ingestion to feature engineering, data management and organization
• Build Agentic AI tools and capabilities for model management and automation
• Guide the team to operating services at internet scale with high availability and reliability
• Propose and implement the best engineering and research practices for scaling ML-powered features, with a goal to enable the fast iteration of and efficient experimentation with novel features
• Contribute to and influence the ML infrastructure roadmap based on the feedback from internal customers
• Project management tools (e.g. Bitbucket, github, JIRA, Microsoft Project, Monday.com) experience

Requirements:

• 8+ years of experience in machine learning and 4+ years of experience in deep learning
• 2+ years of experience in a leadership role
• Experience and understanding of the entire machine learning pipeline from data ingestion to production
• Experience with machine learning operations, software engineering, and architecture
• Experience with large-scale systems including parallel computing
• Experience architecting and building an AI platform that supports productionization of ML models
• Experience with MLOps systems
• Strong programming skills in a scientific computing language such as Python
• Experience using frameworks for machine learning and data science like scikit-learn, pandas, NumPy
• Experience working with ML tools such as Tensorflow, XgBoost, Wavenet, Keras, Numpy and Pytorch
• Extensive experience ML frameworks, libraries, data structure, data modeling, and software architecture in ANN, CNN, RNN with LSTM
• Ability to take successful, complex research ideas from experimentation to production
• Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams

Benefits