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Prodigal is a connected AI platform that helps financial institutions (banks, lenders, credit unions) run their operations. The company has built production AI agents that analyze conversations, guide human agents, conduct customer interactions, and orchestrate outreach across North America. With backing from Y Combinator, Accel, and Menlo Ventures, Prodigal works with 100+ financial institutions and is expanding its AI agent capabilities into originations, document processing, back-office workflows, and servicing.
In this Agent Engineer role, you will build and deploy LLM-powered AI agents (including voice agents) in production for real-world financial use cases. You'll own the end-to-end lifecycle of AI systems—from problem scoping and design through deployment, monitoring, and continuous iteration. Your work will include designing and optimizing prompts, RAG pipelines, and evaluation frameworks to improve accuracy, latency, and reliability. You'll collaborate directly with customers and Implementation Managers to understand workflows, solve real-world problems, and drive successful go-lives and product iterations.
This is a hands-on role with high ownership, fast iteration cycles, and direct impact on enterprise customers and business outcomes. You'll work on real production AI systems (not demos) in a fast-paced, intellectually-stimulating environment.
REQUIREMENTS:
- 1–3 years of experience in backend, data, or AI engineering
- Strong proficiency in Python and SQL
- Experience or exposure to LLMs, RAG, or prompt engineering
- High ownership mentality; ability to work in ambiguity and ship fast
- Comfortable working with customers and cross-functional teams
- Preferred: B.Tech, BE, or M.Tech in Computer Science or related discipline
BONUS QUALIFICATIONS:
- Experience with production LLM systems or AI agents
- Familiarity with evaluation, agent tooling, or voice AI
- Experience with Databricks, MongoDB, or distributed systems
- Startup or customer-facing engineering experience
