Job responsibilities
Leads GenAI strategy and adoption: Spearheads generative AI initiatives, including chatbots and agentic architectures, aligned with business goals.
Collaborates cross-functionally: Integrates ML solutions with data scientists, engineers, and stakeholders to enhance platform capabilities.
Establishes ML lifecycle best practices: Develops standards for model development, deployment, monitoring, and maintenance.
Mentors ML engineering team: Guides and develops the team, fostering continuous learning and innovation.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming language(s)
Proven leadership in ML projects: Demonstrated ability to lead machine learning initiatives and drive strategic AI adoption.
Expertise in GenAI technologies: In-depth knowledge of generative AI, LangChain, LangGraph, Autogen and other orchestration frameworks.
Strong knowledge of MLFlow
Solid understanding of system design and enterprise architecture patterns
Experience in payments: Prior experience working in the financial services industry, particularly in payments or banking, with an understanding of industry-specific challenges and opportunities.
GenAI Implementation Experience: Hands-on experience in implementing Generative AI solutions, such as chatbots or agentic architectures, in a production environment.
Advanced Data Analytics Skills: Proficiency in advanced data analytics and statistical methods, with the ability to derive actionable insights from complex datasets.
Certification in AI/ML
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