As an experienced member of our AI/ML Solution Architecture Group, you'll have numerous opportunities to apply your depth of knowledge and expertise to all aspects of machine learning lifecycle by partnering with your many stakeholders on a daily basis and stay focused on common goals. We embrace a culture of experimentation and intensely strive for continuous improvement and learning. You’ll work in a collaborative, trusting, thought-provoking environment — one that encourages diversity of thought and creative solutions that are in the best interests of our customers globally.
Job Responsibility:
- Engage with business stakeholders to refine requirements, assist with framing business problems as machine learning problems, and help define success metrics
- Define appropriate data engineering processes to facilitate data preparation and feature engineering
- Collaborate with data scientists and data and ML engineers in selecting and evaluating ML models and algorithms using factors such as accuracy, scalability, interpretability, etc.
- Develop end-to-end AI/ML solutions in compliance with and leveraging firm’s policies, standards, and AI/ML governance processes
- Provide technical leadership throughout the entire machine learning lifecycle, in collaboration with architecture, security, risk, operations, and other partner organizations
- Design and lead proof-of-concepts, commercial product evaluations, and custom solutions
Required qualifications, capabilities, and skills
- Bachelor’s degree in computer science, data science, information systems, electrical engineering, or equivalent experience
- Advanced knowledge of architecture, design, and software engineering processes
- Deep understanding and hands-on experience with public cloud technologies, especially with AWS and Azure, and DevOps processes and tools such as Containers, IaC, CI/CD, etc.
- Solid grasp of core algorithms, deep neural networks, and LLMs/SLMs with hands on experience with data/feature engineering, training, orchestration, model deployment/serving, model monitoring, and governance utilizing modern ML frameworks, libraries, and tools
- Strong working experience with big data, data lakes/data mesh/lake house architectures, and ML data engineering processes, tools & techniques
- Keep abreast of advancements in technology and industry trends in the AI/ML space to be able to leverage the right stack for any given problem
- Collaborate with product management, platform engineering, security, risk management, and other partner organizations in discovery and requirements definition
- Leverage agile practices in continuously improving our delivery quality and velocity
- Work in large, collaborative teams to achieve organizational goals