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 or higher in computer science, data science, information systems, or electrical engineering and 10+ years of applied experience in software architecture and machine learning systems.
Experience with core machine learning algorithms, deep neural networks, NLP, and LLMs/SLMs.
Advanced knowledge of software architecture and development for machine learning systems with hands on experience throughout the entire machine learning lifecycle.
Deep understanding and hands-on experience with public cloud technologies, especially with AWS & its machine learning stack (Azure OpenAI experience would be a strong plus).
Working experience with big data, data lakes/data mesh/lake house architectures, and ML data engineering processes, tools & techniques.
Significant experience with one or more programming languages, at least Python and Java, and DevOps processes and tools such as Containers, Iac, CI/CD, etc.