Agent Engineer

About Prodigal

Prodigal is the connected AI platform leading financial institutions use to run their operations.

We work with banks, lenders, credit unions, and other financial companies that lend money to people and manage those relationships over time.

These institutions make millions of high-stakes decisions every day. Who should they reach? When should they reach them? What should they say or offer? When should a case move to a human? How should that change based on the borrower, the account, previous interactions, and the regulations involved?

Getting those decisions right requires a deep understanding of the people, processes, rules, and edge cases behind them.

Prodigal has spent the last eight years building that understanding. More than a billion interactions between financial institutions and their customers have shaped the intelligence, guardrails, and AI agents we now run in production across North America.

Today, our AI agents analyze conversations, capture context, guide human agents, decide the next action, conduct customer conversations, orchestrate outreach, and help people complete payments and resolutions. They are connected, so what is learned in interaction can inform what happens next.

We are expanding this swarm of AI agents across more of the work financial institutions do: originations, document processing, back-office workflows, servicing, and other critical operations where money, identity, people, and regulation intersect.

We are backed by Y Combinator, Accel, and Menlo Ventures, and work with 100+ financial institutions across North America. Listen directly from our CTO, Cofounder - Sangram Raje

What You’ll Do

• Build and deploy

LLM-powered AI agents (including voice agents)

in production, designed for real-world use cases

• Own the

end-to-end lifecycle

of AI systems — from problem scoping and design to deployment, monitoring, and continuous iteration

• Design and optimize

prompts, RAG pipelines, and evaluation frameworks

to improve accuracy, latency, and reliability

• Work directly with

customers and Implementation Managers (IMs)

to understand workflows, solve real-world problems, and drive successful go-lives and iterations

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What We’re Looking For

• 1–3 years experience in

backend / data / AI engineering

• Strong in

Python + SQL

• Experience or exposure to

LLMs, RAG, or prompt engineering

• High ownership, ability to

work in ambiguity and ship fast

• Comfortable working with

customers and cross-functional teams

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Preferred Qualification

• B.Tech, BE, or M.Tech in Computer Science or a related discipline, with 1–3 years of relevant experience

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Bonus

• Experience with

production LLM systems / AI agents

• Familiarity with

evaluation, agent tooling, or voice AI

• Experience with

Databricks, MongoDB, or distributed systems

• Startup or

customer-facing engineering

experience

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Why This Role

• Work on

real production AI systems (not demos)

End-to-end ownership

and fast iteration cycles

• Direct impact on

enterprise customers and business outcomes

Mode of Work - In-Office

From day 1, Prodigal has been defined by talented, humble, and hungry leaders and we want this mindset and culture to continue to blossom from top to bottom in the company. If you have an entrepreneurial spirit and want to work in a fast-paced, intellectually-stimulating environment where you will be pushed to grow, then please reach out because we are looking to build a transformational company that reinvents of the biggest industries in the US.

To learn more about us - please visit the following:

Our Story -

What shapes our thinking -

Our website -

• 1–3 years experience in backend / data / AI engineering
• Strong in Python + SQL
• Experience or exposure to LLMs, RAG, or prompt engineering
• High ownership, ability to work in ambiguity and ship fast
• Comfortable working with customers and cross-functional teams
• B.Tech, BE, or M.Tech in Computer Science or a related discipline, with 1–3 years of relevant experience
• Experience with production LLM systems / AI agents
• Familiarity with evaluation, agent tooling, or voice AI
• Experience with Databricks, MongoDB, or distributed systems
• Startup or customer-facing engineering experience