Lead Applied AI Engineer

Responsibilities

• Deliver agentic AI solutions in production.
• Design, build, and ship AI agents for a partner business domain using the team’s internal platform and reusable components — owning the solution from first workshop to production operation.
• Partner deeply with the business. Embed with business organizations to understand their workflows, data, and operational touchpoints; identify where agentic AI delivers measurable improvement; and translate domain knowledge into technical designs.
• Build quickly and iterate. Compose solutions from platform capabilities rather than rebuilding from scratch; prototype fast, validate with users, then harden for production. Speed with rigor is the job.
• Apply state-of-the-art techniques. Use foundation models, LLM orchestration, retrieval, and transformer-based approaches for sequential and transactional data — choosing the simplest technique that ships.
• Own production quality. Build the evaluation, observability, and guardrail systems for everything you ship; ensure deployed agents meet the bank’s reliability, security, and compliance standards.
• Strengthen the platform. Contribute improvements, patterns, and reusable components back to the platform team so every delivery makes the next one faster.
• Communicate outcomes. Translate technical progress into presentable results — supporting partner stakeholder reviews and executive demos with clarity and credibility.

• 5+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 5+ years of software engineering experience, with at least 3 years applying machine learning or AI to production systems.
• Hands‑on experience building and deploying machine learning models, including transformer‑based architectures or foundation models, in a production environment.
• Strong full‑stack software engineering skills—comfortable across data, models, services, and integration; able to ship end‑to‑end.
• Solid understanding of modern AI evaluation, observability, and guardrails for production deployment.
• Excellent collaboration, technical communication, and problem‑solving skills, with a track record of shipping software that touches real users in real production environments.
• Prior experience in financial services applications (e.g., banking operations, complaints, wholesale operations) or comparable regulated enterprise domains.
• Experience working under Model Risk Management or compliance governance for production AI deployment.
• Experience working with large‑scale transactional or sequential data.
• Experience working in a partner‑facing engineering engagement model with business organizations.

Demonstrates expertise in delivering agentic AI solutions, with a strong focus on building and deploying machine learning models in production environments. Proficient in collaborating with business stakeholders to translate domain knowledge into effective technical designs while ensuring compliance and quality standards are met.