Machine Learning Engineer

About Credit Saison India

Established in 2019, Credit Saison India (CS India) is one of the country's fastest growing Non-Bank Financial Company (NBFC) lenders, with verticals in wholesale, direct lending and tech-enabled partnerships with Non-Bank Financial Companies (NBFCs) and fintechs. Its tech-enabled model coupled with underwriting capability facilitates lending at scale, meeting India's huge gap for credit, especially with underserved and under penetrated segments of the population.

Credit Saison India is committed to growing as a lender and evolving its offerings in India for the long-term for MSMEs, households, individuals and more. Credit Saison India is registered with the Reserve Bank of India (RBI) and has an AAA rating from CRISIL (a subsidiary of S&P Global) and CARE Ratings. Currently, Credit Saison India has a branch network of 80+ physical offices, 2.06 million active loans, an AUM of over US$2B and an employee base of about 1,400 employees.

Credit Saison India is part of Saison International, a global financial company with a mission to bring people, partners and technology together, creating resilient and innovative financial solutions for positive impact. Across its business arms of lending and corporate venture capital, Saison International is committed to being a transformative partner in creating opportunities and enabling the dreams of people.

Saison International is the international headquarters (IHQ) of Credit Saison Company Limited, founded in 1951 and one of Japan's largest lending conglomerates with over 70 years of history and listed on the Tokyo Stock Exchange. The Company has evolved from a credit-card issuer to a diversified financial services provider across payments, leasing, finance, real estate and entertainment. Based in Singapore, Saison International's global operations span over Singapore, India, Indonesia, Thailand, Vietnam, Mexico, Brazil, with active investments in debt, equity, corporate venture capital, and technology.

Experience: 2–3 Years

Department: Data & ML Platform

Location: Bangalore

About the Team

Our Data & ML Platform team powers the core intelligence of our lending operations. We own critical real-time decisioning systems that process 100,000+ requests daily with strict sub-second latency SLAs. These systems drive underwriting, credit risk, and fraud detection across 30+ Loan Product Categories. We operate a highly scalable, event-driven cloud architecture on AWS and are looking for engineers who excel at bridging the gap between data science and robust software engineering.

The Role

We are looking for a high-impact Machine Learning Engineer with 2-3 years of experience to help us deploy, scale, and maintain our production ML systems. In this role, your primary focus will be on the engineering and infrastructure lifecycle of machine learning. You will take predictive models built by our Data Science team, containerize them, optimize them for high concurrency, and deploy them as highly available REST APIs on our AWS cloud infrastructure.

Key Responsibilities

• Containerization & Deployment: Package machine learning models (XGBoost, LightGBM, Scikit-learn) into efficient Docker containers. Deploy and orchestrate these containers using AWS ECS, EKS (Kubernetes), or similar cloud-native platforms.
• Building Scalable APIs: Design, build, and maintain high-throughput, low-latency RESTful APIs (using FastAPI/Flask) to serve real-time model inference to downstream decisioning engines.
• Cloud Engineering & Infrastructure: Architect and manage resilient cloud infrastructure on AWS. Implement Infrastructure as Code (IaC) using Terraform or AWS CloudFormation to automate deployment environments.
• MLOps & CI/CD: Build robust CI/CD pipelines (via GitHub Actions, Jenkins, or GitLab CI) for automated model testing, integration, and continuous deployment. Manage model versioning and registry.
• System Reliability at Scale: Monitor model API performance, optimize memory usage, and configure auto-scaling rules to handle traffic spikes. Integrate asynchronous logging (Kafka/Kinesis) to ensure audit compliance without impacting API response times.
• Code Refactoring: Partner closely with Data Scientists to refactor experimental notebook code into modular, production-ready, and fully tested Python code.

Basic Qualifications

• Experience: 2 to 3 years of hands-on experience in Machine Learning Engineering, Backend Engineering, or Cloud Infrastructure with a strong focus on serving ML models.
• Programming: Advanced proficiency in Python (writing production-grade, object-oriented code) and API frameworks (FastAPI, Flask, etc.). Proficient in SQL.
• Containerization & Orchestration: Deep hands-on experience with Docker. Experience managing containerized workloads using AWS ECS, Kubernetes (EKS), or Docker Swarm.
• Cloud Platforms: Solid practical experience with AWS core services (EC2, S3, IAM, CloudWatch, Lambda, Application Load Balancers).
• Software Engineering Practices: Strong understanding of version control (Git), unit testing, and CI/CD automation.

Preferred Qualifications (Stand-Out Skills)

• Big Data & Databricks: Experience with Apache Spark (PySpark) and the Databricks ecosystem (Delta Lake, Databricks Model Serving, MLflow) is a strong plus.
• High-Performance Databases: Familiarity with real-time NoSQL databases (e.g., DynamoDB, Redis) for sub-millisecond feature lookups during model inference.
• Event-Driven Architectures: Experience working with high-throughput messaging queues (Apache Kafka, AWS Kinesis, or SQS).
• Domain Knowledge: Previous experience in FinTech, lending, or building high-availability financial systems.

What We Offer

• Opportunity to work on high-stakes, real-time systems where your cloud engineering directly impacts the company's daily revenue and risk exposure.
• A culture that values automation, strict engineering standards, and scalable architectural design.
• Collaboration with cross-functional teams of highly skilled Data Engineers and Data Scientists.