🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
We are seeking a deeply technical Backend SDE-4 / Principal Engineer to join our global engineering team at a leading Fintech product company operating in the US and India. In this critical, hands-on leadership role, you will work at the intersection of applied machine learning and platform engineering. You will not be building AI chatbots or LLM wrappers; you will be solving the hardest architectural and performance problems across our ML infrastructure. Utilizing a combination of Python and Node.js, you will design scalable tools and systems that accelerate model development. You will ensure our backend architecture can handle massive financial datasets and meet the rigorous security, latency, and performance standards of the financial sector. Key ResponsibilitiesAdvanced ML System Design & Infrastructure Architectural Leadership: Architect the end-to-end platform for our ML systems. Design for high availability, fault tolerance, and extreme scalability to serve multiple business domains within the firm. High-Performance Pipelines: Design and build ML systems and reproducible, high-performance training pipelines across our infrastructure. Inference Architecture: Architect inference systems that balance latency, throughput, cost, and reliability at scale. Define the abstractions, APIs, and reusable components that make solution-building repeatable. Platform Innovation: Build a unified platform for training, serving, and managing representations at scale, and streamline feature engineering pipelines to reduce manual steps. Deep Learning & Machine Learning Engineering Algorithm Implementation: Apply state-of-the-art techniques and core ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost) to extract structured insights from complex financial data. Model Optimization: Implement automated continuous-learning systems that handle data refresh, retraining, evaluation, and monitoring to improve predictive accuracy in production. Backend Integration: Collaborate closely with data scientists and software engineers to integrate ML systems cleanly into backend products. Technical Leadership & Mentorship Engineering Standard: Establish standards for evaluation, observability, guardrails, and production reliability across the platform. Cross-Functional Collaboration: Bridge engineering and science teams, influencing technical strategy and roadmaps for our next-generation ML Platform. Mentorship: Mentor senior engineers and raise the technical ceiling of the team, actively prototyping and shipping the most complex components. Security & Compliance (Fintech Focus) Data Security: Enforce strict data privacy and security measures, crucial for processing sensitive financial data in a regulated, enterprise-scale environment. Fintech Compliance: Ensure all system designs and ML integrations comply with US and Indian financial regulations and standards. What We Are Looking For Experience: 10+ years of progressive software engineering experience, with significant time in a Principal or Lead Architect role focusing on ML/DL infrastructure. Experience in high-scale, ML-driven product environmentsespecially in fintech, pricing, or risk modelingis highly preferred. Deep Learning Expertise: Strong theoretical and practical foundation in machine learning and statistics, with the ability to reason from first principles about model tradeoffs and evaluation. Backend Mastery: Deep, production-level expertise in both Python (for ML and data processing) and Node.js/TypeScript (for scalable APIs and microservices). Distributed Systems: Experience building scalable ML pipelines using MLOps best practices and cloud computing platforms (e.g., AWS, GCP). Foundational Knowledge: Deep knowledge of math, probability, statistics, data structures, and algorithms. What We Offer Industry-Leading Compensation: Up to 70 LPA, commensurate with your exceptional skills and experience. Global Exposure: Work directly with a dynamic US-based fintech client on critical, high-impact systems. Leadership Impact: The autonomy to shape the technical direction of the company and solve the hardest architectural problems. Comprehensive Benefits: Top-tier health insurance, flexible working arrangements (hybrid from Bangalore), and support for continuous professional development. Skills: fintech,ml,design,neural networks,data,security,machine learning .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.