Machine Learning Engineer

About Credit Saison IndiaEstablished 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 with a vision to unlock India’s financial potential. Combining our tech-enabled model with underwriting capability has allowed us to lending at scale to meet India’s huge gap for credit, especially with underserved and under penetrated segments of the population. We continue to be on a mission to revolutionize the Indian lending landscape after reaching the Top 50 lenders in India in FY23.At CS India, we are committed to growing as a lender and evolving our offerings in India for the long-term for MSMEs, households, individuals and more. Being registered with the Reserve Bank of India (RBI) and one of the few marquee lenders in India that can claim to have an AAA rating from CRISIL (a subsidiary of S&P Global) and CARE Ratings, we are one of the strongest financial houses in India. having a branch network of 45 physical offices, 1.2 million active loans, an AUM of over US$1.5B and an employee base of about 1,000 people.Credit Saison India (CS 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.Based in Singapore, over 1,000 employees work across Saison’s global operations spanning Singapore, India, Indonesia, Thailand, Vietnam, Mexico, Brazil.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.Job Description: ML Engineer / Sr. ML EngineerRoles & Responsibilities:Design, build, and maintain high-throughput, low-latency real-time and batch feature computation pipelines.Collaborate with Data Scientists to deploy, profile, and optimize complex models for high-throughput inference workloads.Own end-to-end production observability by building robust tracing tools, alerting systems, and monitoring dashboards.Lead technical integrations for complex data products, supporting the design and implementation of real-time decisioning models.Define infrastructure requirements and manage infrastructure optimization for scalable machine learning deployments.Partner with cross-functional stakeholders to leverage internal and external data effectively to drive impact through ML solutions.Architect and implement systems that support complex model topologies, including ensembles, chaining, shadow deployments, and champion-challenger setups.Apply foundational AI technologies and modern frameworks to solve critical business problems.

Required skills & Qualifications:2–5 years of hands-on experience deploying machine learning models into production real-time environments.Bachelor’s, Master’s, or PhD in a STEM discipline from a top-tier institution.Strong problem-solving abilities with a focus on platform engineering and product development.Deep knowledge of distributed real-time computing and inference serving architectures.Solid comprehension of core machine learning algorithms, artificial neural networks, and deep learning techniques.Strong proficiency in Python, SQL, Model Serving Frameworks (TF Serving, ONNX), Cloud Platforms (AWS ecosystem), MLOps tools (MLflow, Kubeflow), and Docker.Commitment to software engineering best practices, quality control, and modern development tools (including AI coding assistants like Claude Code, Codex, etc.).Familiarity with standard ML libraries (scikit-learn, PyTorch) and Model Explainability tools (SHAP, LIME).Experience with distributed data processing frameworks (e.g., Apache Spark) and exposure to Databricks is highly advantageous.Familiarity with compliance frameworks and model risk management practices is preferred.Proven track record of deploying and maintaining ML systems within Financial Services, Fintech, or Lending domains is highly desired.Excellent written and verbal communication skills for cross-functional collaboration.