Machine Learning Engineer - Python

Role : Machine Learning Engineer

Job Description :

Solving for the next billion users globally, True Balance is tackling complex problems in a rapidly growing fintech domain using massive data pipelines and creative solutions across Growth, Lead Generation, Underwriting, Collections, and beyond.

Since traditional financial scoring and legacy optimization methods are insufficient to bridge the global financial inclusion gap, building a next-generation platform for low-cost, ultra-accessible financial services to improve people's lives warrants modern, cutting-edge ML utilizing petabyte-scale data.

Our highly motivated Machine Learning Engineers thrive on these challenging problems, deploying sophisticated models that directly impact and scale various aspects of our core business.

Currently we are developing a few cutting-edge products like LIP (Lending Intelligence Platform).

We strongly believe the future of the lending industry will be completely automated, hyper-personalized, and powered by real-time alternative data.

If you are a critical thinker with experience in machine learning workflows, passionate about solving business problems using data and thrive in a dynamic, creative, and collaborative environment, we are searching for you.

This is not a role where your work finishes with a Jupyter Notebook, tweaking hyperparameters, and throwing a pickle file over the wall to DevOps.

At True Balance you build resilient AI systems that won't melt under millions of concurrent production requests.

You are an Engineer, with a deep specialization in the unique constraints of productionizing Machine Learning.

The Foundations:

• Strong background in fundamentals concurrency, distributed systems, memory management, data structures, and system design.

The Production Stack:

• 5+ years of experience shipping production-grade code.
• Deep mastery of Python and either Go, Java, or C++.

ML Infrastructure:

• Hands-on experience with production ML serving frameworks (Triton, TorchScript, BentoML) and MLOps platforms (MLflow, Kubeflow).
• You know how to build CI/CD for ML.
• You don't just write code; you write automated tests for model behavior, input validation, and fallback mechanisms.

Roles and responsibilities:

Architect Inference Infrastructure:

• Own the end-to-end performance of our LIP execution layer.
• You will design and build highly scalable microservices that serve models, ensure predictive accuracy, and maintain a strict execution budget per request.

Engineer Real-Time Feature Streaming:

• Design, optimize, and maintain distributed, event-driven data pipelines (using Flink, Kafka, Spark) to extract and aggregate alternative data features on the fly.
• You will turn chaotic user activity streams into clean, structured inputs for our models in real time.

Build Next-Gen MLOps Ecosystems:

• Develop automated, robust CI/CD frameworks specifically for machine learning.
• This includes building self-healing pipeline abstractions, automated model retraining loops, and automated shadow-testing frameworks where new models can safely run alongside production models before a full rollout.

Eliminate Drift and Data Skew:

• Implement rigorous monitoring, alerting, and automated validation systems to catch training-serving data skew, input anomalies, and feature drift before they impact underwriting or risk decisions.