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We are seeking an experienced AI Solutions & Delivery Engineer to join our Business Controls Group (BCG) as a hands-on technical leader focused on applying Artificial Intelligence, including Generative AI and Agentic AI, to accelerate and elevate first line of defense (1LOD) risk and control operations.
The ideal candidate will possess a blend of business and technical savvy, a vision for what AI can unlock within a controls environment, and the drive to transform that vision into reality. You will be responsible for partnering with stakeholders across BCG: listening, facilitating, hands-on developing, and delivering prioritized AI solutions that augment and elevate the capabilities of our risk and controls professionals.
You will focus on building AI-powered solutions for specific BCG initiatives, including control testing, horizontal risk-related reporting, and other 1LOD process acceleration activities, and manage a portfolio of AI initiatives from ideation through production deployment. This role exists to increase the quality, velocity, and scalability of how our Business Controls teams execute their work.
You will also serve as technical lead for the Business Controls AI Engineering function, acting as a system-level thinker, designing, orchestrating, building, and deploying AI agents, tools, and enterprise data integrations for BCG and 1LOD risk management teams within the guardrails of SoFi’s governance framework.
What you’ll do:AI Strategy & Solutions DeliveryPartner with BCG leadership to define the AI solutions vision and roadmap for 1LOD risk and controls, based on business use case prioritization and scorecard evaluation.
Identify and implement opportunities where AI can uplift risk and control processes and practices, with an initial focus on control testing velocity and business controls reporting.
Design, develop, and deploy AI-powered solutions (including LLM-based agents, automated evidence analysis, and intelligent workflow tools) that deliver measurable improvements to BCG operations.
Translate ambiguous, fast-evolving AI work into clear narratives, artifacts, and decision frameworks for senior and executive leaders.
Manage the end-to-end AI product lifecycle, from ideation and requirements gathering through development, testing, launch, and post-launch optimization.
Partner with Business Controls teams, risk professionals, technology teams, and other BCG stakeholders to drive development, adoption, and continuous improvement of AI solutions.
Enable risk professionals’ adoption of AI tools by providing training, documentation, and hands-on support to increase comfort and proficiency across the organization.
Partner with enterprise AI and platform engineering teams to ensure AI solutions are built in compliance with best practices, enterprise policies, and internal standards.
Contribute to the uplifting and upscaling of AI capabilities across BCG, assisting in the responsible adoption of best-in-class AI platforms within the organization.
Define key performance indicators (KPIs) and metrics for AI solutions, monitor performance post-launch, and iterate based on data-driven insights.
Ensure AI solutions deliver measurable business value, including reductions in cycle time, improvements in testing coverage, and enhanced reporting accuracy.
Establish and maintain dashboards that communicate AI initiative progress, adoption, and impact to BCG leadership.
Manage end-to-end Generative AI and Responsible AI governance for BCG solutions, including documentation, validation, monitoring, explainability, and adherence to internal policies and emerging AI regulations.
Demonstrate critical thinking and the ability to clearly articulate risks and dimension impact when evaluating AI use cases and deployments.
Ensure all AI solutions align with enterprise governance standards, regulatory expectations, and model risk management requirements.
Serve as technical lead for the Business Controls AI Engineering function.
Evaluate, select, and integrate AI/ML tools and platforms that best serve BCG’s operational needs.
Break ambiguous problems into clear, measurable components and define actionable AI development plans.
Demonstrate sound judgment when balancing innovation against risk in a regulated financial services environment.
8+ years of experience in risk management, controls, compliance, or technology roles, with at least 3+ years focused on AI/ML products, applied AI solutions, or AI-driven process transformation.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent work experience).
Demonstrated experience in ambiguous or emerging domains, including leadership in defining, launching, and scaling AI solutions that deliver significant business impact.
Proven experience working at a senior individual contributor level or equivalent in a regulated organization, preferably in financial services.
Strong understanding of the financial services industry, including 1LOD risk management, control testing, and business controls operations.
Hands-on experience with AI/ML concepts including LLMs, Generative AI, Agentic AI, supervised/unsupervised learning, model monitoring, and responsible AI practices.
Understanding of data governance, data pipelines, and MLOps principles.
Strong communication and executive stakeholder engagement skills, with the ability to explain complex ideas clearly to non-experts at different audience levels.
Experience with regulatory expectations, model governance, and risk management in a banking or financial services context.
Strategic thinker with a strong analytical and problem-solving mindset.
Rapid self-learner in unfamiliar domains with a bias toward action and delivery.
Experience with Snowflake, dbt, or other modern data platform tools used in enterprise data environments.
Familiarity with LLM tooling, RAG systems, AI agent frameworks, or AI-assisted data workflows.
Financial services experience across domains such as Credit, Fraud, Collections, or Operational Risk.
Experience with GRC platforms and control testing methodologies.
Experience implementing AI governance frameworks at scale.
AWS experience (S3, Glue, Lambda) and infrastructure-as-code familiarity.
