ML&Data Ops 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 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.

Roles & Responsibilities

• Design, build, and maintain balanced Data, ML, and AI engineering pipelines to automate data provisioning, model deployment, and enterprise workflow execution.
• Implement and support robust LLMOps and MLOps practices across data, machine learning, and Generative AI systems to automate model evaluation, monitoring, and CI/CD workflows.
• Manage, optimize, and scale Databricks workspace configurations, clusters, and jobs for enterprise data processing and AI/ML workloads.
• Collaborate cross-functionally with data engineers, ML engineers, software engineers, and product leads to design, deploy, and scale data pipelines, feature stores, and ML serving systems into production.
• Implement proactive incident response, event instrumentation, and self-healing mechanisms to detect and remediate system anomalies or data/model quality issues.
• Provide day-to-day operational support, infrastructure upgrades, capacity planning, and cloud resource optimization for data platforms and ML infrastructure.
• Work closely with IT DevOps, SRE, and security teams to enforce governance, data lineage, compliance, and enterprise CI/CD deployment standards.
• Promote engineering best practices, conduct code reviews, participate in on-call rotation support, and contribute to knowledge sharing across teams.
• Develop hands-on Proofs of Concept (POCs) for modern data platforms, feature stores, and real-time streaming tools in collaboration with product and analytics teams.
• Maintain agility towards evolving technology stacks across DataOps and MLOps platforms to continually modernize infrastructure.

Required Skills & Qualifications

• Strong problem-solving mindset, exceptional cross-functional communication, and a track record of driving collaborative DevOps/DataOps/MLOps culture.
• Solid understanding of modern DevOps, MLOps, and DataOps methodologies, including CI/CD automation, model governance, and observability tools (e.g., MLflow, Weights & Biases, LangSmith).
• Hands-on experience with core cloud data & ML services on AWS (e.g., SageMaker, Glue, EMR, Athena, S3) and strong expertise in Databricks management, optimization, and workspace administration.
• Strong understanding of database fundamentals, replication, relational/NoSQL databases, and vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate).
• Proven experience building CI/CD automation pipelines for containerized Python, Java, or Scala microservices and ML serving systems.
• Proficient with Git version control and standard branching workflows.
• Practical experience deploying, monitoring, and debugging distributed data pipelines, ETL workflows, and model deployment systems.
• Familiarity with configuration management and provisioning tools (e.g., Terraform, CloudFormation, Ansible).
• Scripting proficiency in one or more languages (Python, Bash, or JavaScript).
• Practical experience with containerization using Docker and exposure to Kubernetes orchestration.
• Solid grasp of machine learning lifecycle, deep learning frameworks (PyTorch, TensorFlow), NLP/CV concepts, and feature engineering workflows.
• Bachelor's Degree in Computer Science, Software Engineering, or a related technical discipline from a top-tier engineering college/university (tier-1 preferred).
• Experience: 2 – 5 years of experience across DataOps, MLOps, ML Engineering, or Data Engineering in enterprise cloud environments.