Machine Learning Engineer
Prodigal
Salary undisclosed
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Description
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 one 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.
About the DSML Team
The Data Science and Machine Learning (DSML) team owns the full lifecycle of Prodigal's ML systems, from data curation to model selection, fine-tuning, and post-training, through to deployment, evaluation, and ongoing monitoring. We sit close to real product and customer problems, and we're expected to move from experiment to production with both speed and rigor.
What You'll Do
- Understand the problem domain. Work closely with product, engineering, and customer-facing teams to turn ambiguous, real-world problems into well-scoped ML problems.
- Curate datasets. Source, clean, and structure the data needed to train and evaluate models, often before any "off-the-shelf" dataset exists.
- Train models across the full spectrum. Build and train everything from simple classifiers to neural networks, transformers, and small/large language models, choosing the right level of complexity for the problem at hand.
- Deploy and scale for inference. Take models from notebook to production. optimizing for latency, cost, and reliability at scale.
- Evaluate and close the loop. Design evaluation frameworks that actually measure what matters, and build learning loops.
What You Bring
- Strong fundamentals in ML/DL, backed by hands-on experience training and shipping models, not just coursework or competitions.
- High agency, you take an ambiguous problem and run with it rather than waiting for it to be fully specified.
- A first-principles approach to problem-solving – you reason from the ground up rather than defaulting to whatever's fashionable or familiar.
- Genuine curiosity and the comfort to operate at the edge of what you already know.
Why This Role
- Real problems, real models, real data. No toy datasets, no academic benchmarks — you'll work on problems that directly affect customers and the business.
- Frontier technology, applied. Learn to harness the latest in AI and put it to work on problems that matter.
- Smart people, fast shipping. Work alongside a sharp team, ship quickly, and grow faster than you would elsewhere.
What You Bring
- 2-5 years of hands-on engineering experience, with good exposure to building or working with ML or AI systems in production.
- Solid Python fundamentals - you are comfortable writing clean, maintainable code and debugging production issues.
- Some experience working with LLMs: prompt engineering, API integrations, or building simple pipelines or agents.
- High bias for action - you don't wait to be told exactly what to do, and you push yourself to ship rather than over-engineer.
- Eager to learn in a fast-moving environment, take ownership of your work, and ask good questions.
Top Tier Benefits
Health insurance for you and your family, meals at office on us, travel reimbursement, unlimited leaves, gym membership, unlimited learning & development, flexible work schedule and a world-class team to learn and grow with!
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 one of the biggest industries in the US.
To learn more about us - please visit the following:
Our Story - https://www.prodigaltech.com/our-story
What shapes our thinking - https://link.prodigaltech.com/our-thesis
About this role
Prodigal's DSML team owns the full lifecycle of machine learning systems that power AI agents used by over 100 financial institutions across North America. In this role, you'd move from identifying real-world problems in lending and customer servicing to shipping production models that handle billions of interactions. The work spans the entire spectrum: translating ambiguous customer and product needs into concrete ML problems, curating datasets from scratch, training models ranging from simple classifiers to LLMs and transformers, and optimizing them for latency and cost at scale.
You'd need 2–5 years of hands-on experience building or working with ML systems in production, solid Python skills, and some exposure to LLMs—whether through prompt engineering, API work, or building simple agents. Beyond technical chops, the role rewards high agency and first-principles thinking: you take ownership of ambiguous problems, ship rather than over-engineer, and stay curious at the edge of what you know. This is in-office work in Mumbai, and you'd be part of a team that values speed and rigor equally.
The appeal here is straightforward: real data, real customers, real business impact—no toy datasets or academic benchmarks. You'd work alongside sharp engineers on frontier AI technology applied to one of the largest industries in the US, with the backing of Y Combinator, Accel, and Menlo Ventures behind you.
Typical pay for this role
$140,000
Typical range: $120,000 to $140,000
This role's local market on JobMinglr
Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Machine Learning Engineer postings in Mumbai, and analyze any offer before you accept it.
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