Senior Engineer, AI

About the role

Work collaboratively with technology and business groups to design and develop machine learning and AI solutions for financial use cases, leading technical architecture for both traditional and deep learning models. The role includes executing traditional machine learning solutions, as well as developing and integrating generative AI and agentic AI technologies to automate workflows, enhance decision-making, and enable intelligent systems within financial applications.

Responsibilities

• Conceive, design, engineer, and implement ML and AI solutions by studying information needs; conferring with users; studying systems flow, data usage, and work processes; investigating problem areas;
• Routinely demonstrate initiative and creativity in developing technology solutions;
• Serve as technical expert or lead projects/programs and technical staff to develop, test and implement significant new products, or operational improvements or devise new approaches to problems at the division/business unit.
• Mentor junior engineers.
• Design secure, scalable, and compliant architectures for AI/ML, generative AI, and agentic AI solutions for finance use cases.
• Select and integrate AWS services for data pipelines, model hosting, vector search, orchestration, monitoring, and governance.
• Establish deployment standards, CI/CD, observability, and cost controls.
• Build and deploy predictive analytics, ML, and gen AI solutions into production.
• Develop robust data/model pipelines, APIs, and integration layers. Implement MLOps practices for training, validation, deployment, and monitoring.
• Contribute to agentic workflows, tool use, and orchestration patterns.
• Select and integrate AWS services for data pipelines, model hosting, vector search, orchestration, monitoring, and governance.

Qualifications

• Bachelor’s Degree with 6 years’ experience; Master’s Degree with 5 years’ experience; PhD with 0 years’ experience.
• Possess thorough theoretical and practical understanding of python, data science, machine learning, generative AI and agentic AI approaches and techniques.
• Experience architecting solutions in an AWS environment.
• Experience developing robust data/model pipelines, APIs, and integration layers. Implement MLOps practices for training, validation, deployment, and monitoring.
• Proven experience with designing secure, scalable, and compliant architectures for AI/ML, generative AI, and agentic AI solutions for various use cases.
• Proven experience designing AL/ML and GenAI solutions using python.
• Proven experience with selecting and integrating AWS services for data pipelines, model hosting, vector search, orchestration, monitoring, and governance.
• Proven experience with establishing deployment standards, CI/CD and observability using GitHub and AWS.
• Proven implementation of creative technology solutions that advanced business.

Preferred

• AWS certifications for architecture or AI
• Excellent written and oral English communication skills
• Strong cross functional collaboration skills