AI / ML Integration Engineer

TL;DR: Full-time AI/ML integration engineering position. Major work around our F1 categorisation engine. Must have 4+ years of experience in Python, FastAPI, databases and MLOPs.

🏦 About Fold

At Fold, we are rebuilding personal finance and mobile banking from the ground up for India’s internet native citizens.

🏋️ About the role

We are seeking a talented AI/ML integration engineer to join our dynamic tech team. As an AI/ML integration engineer, you will be responsible for building and maintaining ML services that will help our ML models to work efficiently. You will work closely with our backend and ML team to integrate machine learning capabilities into Fold. You will also collaborate with cross-functional teams to design and deploy scalable ML solutions that deliver value to our users.

💯 You’d fit in if

  • We don’t care about your credentials and degrees, but having a solid 4+ years of experience in building and managing Python FastAPI web servers at scale is must.
  • You enjoy building ML-powered products from scratch. Joining in at an early stage involves a lot more than just coding. You'll be working in often fast-paced environments, contributing to product and business decisions, and leveraging your expertise to drive innovation.
  • You can work in teams. You need to be able to collaborate and work across teams, effectively communicating complex ML concepts to both technical and non-technical stakeholders.
  • You can work within business constraints. You understand how companies work and can balance the tradeoffs between time, speed, and features when implementing ML solutions.
  • You have a deep understanding of the fintech industry. Experience integrating ML techniques to fintech applications is a big plus.
  • You have been a Fold user and interested in solving personal finance, categorisation problems at scale.

🛠 Responsibilities

  • Develop and implement machine learning models to build and improve our categorisation engine F1.
  • Work closely with our ML team and Integrate AI/ML based solutions seamlessly into our core product.
  • Design and build APIs and infrastructure to serve ML models as well as LLM based solution on scale.
  • Optimize ML models for deployment in production environments, considering factors such as latency, memory usage, and resource constraints.
  • Setup data pipelines for EDAs, training and development of models.
  • Collaborate with cross-functional teams to define and implement data collection strategies, ensuring high-quality and relevant data for training and evaluation.
  • You'll create experiences that shape an iconic product. We believe in hiring smart people that pride themselves in good values, their work ethics, and holding great responsibility.

🤌🏻 Good to have skills

Apart from building APIs, hosting models, maintaining the MLOPs/infra, having knowledge of Machine Learning Models, LLMs and ML stack is a big plus.

⚙️ Our tech stack

  • Back-end: Python, FastAPI, Postgres, Redis, AWS SQS, Qdrant, Meilisearch
  • Infra: Amazon AWS, ECS, Terraform
  • CI/CD: GitHub actions
  • Monitoring: Grafana, X-Ray, Cloudwatch

🌱 Join us

  • Full-time
  • We'll help set up your workspace
  • Competitive salary
  • Generous stock options (for full-time employees)

🎁 Perks and benefits

  • Generous leave policy: Unlimited paid leave.
  • Flexible working hours: It doesn't matter if you're a morning person or a night owl, work when you want. We all work asynchronously. Meetings are the exception, not the rule.
  • Get your perfect setup: Mac/Windows/Linux, mechanical keyboard or anything that you need to do the best work of your life. We'll help set up your workspace.
  • We'll take care of you: Annual team retreats, meals and accommodation if you choose to work at Bangalore HQ. Wellness allowance to take care of your physical and mental health (gym memberships, meditation apps, and anything you need).
  • Health insurance & benefits: Comprehensive health insurance coverage for full-time employees, includes free health check-ups, unlimited doctor consultations, and generous personal accident insurance.
  • Make a big difference: Take, own, and implement decisions to build Fold from scratch. We are not rushing to market but focusing on a quality product and the attention to little details.

⚙️ Our recruitment process

  1. Introduction: Get on an introductory call with us. We'll talk about your interests, your past experience, our vision of the future, and how you can contribute and help us achieve it.
  2. Take-home exercise: We'll send over an application development challenge, which you'll have to complete and send back in 48 hours.
  3. Pair programming: If we like your submission, we'll invite you to an interview with one of our engineers (strictly no white-boarding), with whom you'll be pair-programming on a real-world problem.

We move very fast, and we'll be mindful of your time. All of this will be done within a week, and if all goes well you'll receive a final offer within 24 hours of the pair-programming round.

✍️ Write to us

We are committed to assembling an unrivalled team of builders, artists, technologists, and adventurers who aim to create a new way to explore the world. As an early crew member, you'll have an enormous impact on both our product and company culture.

If you think you'd like to join us, write to us at join@fold.money, with links to your GitHub profile, Devfolio, personal portfolio, LinkedIn, or anything else you think might be relevant. To truly stand out, we recommend you answer at least two of these questions:

  • List three problems in 21st-century personal finance, and tell us how you would solve them.
  • Are you experienced in deploying machine learning models in production environments? If so, what tools and techniques have you used?
  • What are the most impactful machine learning projects you've worked on?
  • Can you share an example of a complex problem you solved using machine learning techniques?
  • How do you evaluate the performance and interpret the results of your machine learning models?
  • Have you worked with large-scale datasets and distributed computing frameworks for training ML models? If yes, please explain.