Applied AI ML Senior Associate

What You'll Do

• Architect and implement distributed AI/ML infrastructure, including inference, training, scheduling, orchestration, and storage.
• Integrate Generative AI and Classical AI within the ML Platform using state-of-the-art techniques.
• Implement, deliver, and support high-quality ML solutions in partnership with a team of ML Engineers.
• Collaborate with product teams to deliver tailored, AI/ML-driven technology solutions.
• Develop advanced monitoring and management tools for high reliability and scalability in AI/ML systems.
• Optimize AI/ML system performance by identifying and resolving inefficiencies and bottlenecks.
• Lead the entire AI/ML product life cycle through planning, execution, and future development.
• Hire, lead, and mentor a team of Machine Learning and Software Engineers, focusing on best practices in ML engineering.
• Engage deeply in technical aspects, reviewing code, mentoring engineers, troubleshooting production ML applications, and enabling new ideas through rapid prototyping.
• Actively collaborate with Product, Technology, and other cross-functional teams to understand complex business problems and formulate data-driven solutions.
• Design, develop, and deploy machine learning and AI solutions that meet success metrics aligned with business goals, considering constraints like model complexity, scalability, and latency.
• Partner with Risk and Compliance teams to ensure comprehensive model documentation, track performance metrics, and maintain adherence to regulatory compliance standards.
• Translate model outcomes into business impact metrics and communicate complex concepts to senior management and stakeholders.

What You'll Need

• 6+ years of experience in engineering management with a strong technical background in machine learning.
• Extensive hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, JAX, and scikit-learn.
• Deep expertise in Cloud Engineering (AWS, Azure, Google Cloud Platform) and Distributed Micro-service architecture.
• Experience with the Kubernetes ecosystem, including EKS, Helm, and custom operators.
• Background in High Performance Computing, ML Hardware Acceleration (e.g., GPU, TPU, RDMA), or ML for Systems.
• Strategic thinker with the ability to craft and drive a technical vision for maximum business impact.
• BS with 5+ years or MS with 3+ years of hands-on industry experience in Machine Learning.
• Good understanding of the latest advancements in NLP concepts, such as transformer architecture and knowledge distillation.
• Experience in classical ML techniques including classification, clustering, optimization, cross-validation, data wrangling, feature selection, and feature extraction.
• Ability to design experiments, establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously.
• Scientific thinking with the ability to invent and work both independently and in highly collaborative team environments.
• Solid written and spoken communication skills.
• 2 years of hands-on experience with virtual assistant model development and optimization.
• Familiarity with continuous integration models and unit test development.
• Experience with A/B experimentation and data/metric-driven product development.
• Experience with deep learning is a plus.
• Master's degree in a quantitative discipline (e.g., Computer Science, Data Science, Mathematics/Statistics, or Operations Research) with a minimum of 3 years of industry experience.
• Experience with Shell Scripting, Jupyter notebook/Lab, SQL, PySpark, and AWS Cloud Services is required.
• Proficient in Python with hands-on experience in Machine learning and Deep learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., NumPy, Scikit-Learn, Pandas).