About the Role We are looking for a passionate and technically strong AI/ML Engineer to join our core team driving the intelligence behind our lending platform. This is a role where you'll build high-performance ML frameworks, scale decision systems, and lay the foundation for the next evolution: agentic AI systems that autonomously extract insights, support business decisions, and power internal copilots. If you are someone who enjoys solving hard problems at the intersection of software engineering and ML, and are excited about the future of AI agents in real-world systems, we want to hear from you. What You'll Do ● Own and evolve core frameworks powering the loan decision-making process across the company ● Design, develop, and optimize scalable ML infrastructure and software frameworks that integrate seamlessly with production systems ● Partnering with senior engineers and domain leads to lay groundwork for agentic systems – from auto-insight generation to internal decision copilots (e.g., AI for BI) ● Help develop data and model lifecycle: from ingestion and feature engineering to deployment and monitoring ● Build generalized systems that support multi-tenant use across verticals be it policy or data for analytics ● Continuously monitor production performance to ensure that live systems behave as expected, and proactively propose improvements ● Document learnings, scale reusable patterns, and drive adoption of best practices in ML system design You should apply if you ● Have 4+ years of experience building production-grade software systems in Java / Scala / Python ● Have familiarity with ML lifecycle and deployment practices, even if not deep modeling experience ● Exhibit deep business understanding by translating technical capabilities into impactful solutions, ensuring that frameworks contribute directly to business outcomes ● Are comfortable working in a fast-paced, startup environment and taking end-to-end ownership ● Possess a strong sense of compounding, capable of building modular, iterative solutions that grow in capability over time, especially in the context of Agentic AI frameworks Must Have ● 3+ Hands-on experience with developing frameworks from scratch: SDKs, or platforms that serve multiple internal teams ● Strong grasp of OOP design, system architecture, and ML pipeline design ● Demonstrated ability to take a messy real-world problem and build reliable, scalable systems around it ● Builds both production-grade systems and internal frameworks that enhance team productivity, with a focus on delivering overall impact ● Proven ability to work in a cross-functional environment comprising of Data Scientists, Business Analysts, Product managers and Data Engineers Nice to have ● Exposure to agentic or autonomous systems (e.g., LangGraph, CrewAI, AutoGPT-style architectures) ● Experience with internal tools like AI copilot builders, dashboarding frameworks, or data storytelling systems ● Prior work in Fintech, especially in lending, credit risk, or collections workflows ● Experience with distributed data processing tools like Apache Spark for Pipeline setup ● Academic background from top-tier institutions (IIT/NIT/BITS/etc.) Success Metrics ● Build frameworks that consistently perform as expected in production environments, minimizing downtime and firefighting ● Develop tools and systems that measurably increase the productivity and velocity of teammates across data, engineering, and product functions ● Design solutions that are scalable, highly configurable, and easy to adapt to evolving business requirements — reducing turnaround time for changes. ● Lay robust foundations that enable the organization to transition towards Agentic AI adoption — through modular, interoperable, and forward-compatible systems
The role involves building, monitoring, and enhancing fraud risk strategies across the lending lifecycle to minimize losses, safeguard portfolio quality, and ensure regulatory compliance. The candidate will work closely with Credit Risk, Operations, Collections, Technology, and Business teams to proactively manage fraud risks while supporting sustainable growth. What will you do ? Fraud Prevention & Detection Develop and implement fraud risk management strategies for unsecured and secured lending products (e.g., personal loans, consumer durable loans, credit cards, business loans). Set up robust customer onboarding and transaction monitoring controls to detect application fraud, identity theft, bust-out fraud, mule accounts, and other modus operandi. Monitor fraud trends, emerging typologies, and regulatory advisories to ensure preventive measures. Define rules, scorecards, and machine learning models for real-time fraud detection in collaboration with analytics/tech teams. Build efficient rules around fraud management with best in class false positives Fraud Investigation & Monitoring Lead investigations into suspected fraud cases, coordinating with internal stakeholders and external agencies (field investigation teams, police, bureaus, etc.). Establish MIS and dashboards for fraud monitoring, root cause analysis, and reporting. Conduct periodic reviews of high-risk segments, channels, and partners. Governance & Policy Formulate and update the Fraud Risk Management Framework, policies, and SOPs in line with RBI guidelines and industry best practices. Ensure compliance with internal audit and regulatory requirements on fraud risk reporting and management. Liaise with regulators, law enforcement, and industry bodies (e.g., CIBIL, SIDBI, RBI working groups) where required. Collaboration & Stakeholder Management Partner with Credit Risk, Underwriting, Operations, Technology, and Business teams to ensure fraud controls are embedded across the lending lifecycle. Train and sensitize employees, sales channels, and partner institutions on fraud risks and preventive measures. Work with collections/recovery teams for fraud-related recovery actions. Key Skills & Competencies Strong understanding of fraud typologies in lending (application fraud, synthetic ID, collusion fraud, digital lending fraud, account takeover, etc.). Good knowledge of Indian regulatory requirements (RBI Master Directions, KYC norms, Fair Practice Code, Fraud Classification norms, etc.). Analytical mindset with hands-on experience in fraud analytics, rules engines, and transaction monitoring systems . Excellent investigation, problem-solving, and decision-making skills. Strong communication and stakeholder management skills. Ability to work under pressure and manage multiple priorities. Qualifications & Experience Graduate/Postgraduate degree in Engineering, Finance, Risk Management, Economics, or related field (CA/MBA/CFE preferred). 6–10 years of experience in fraud risk management within banks, NBFCs, fintechs, or credit bureaus. Exposure to digital lending ecosystems, credit bureaus, and fraud data consortiums is desirable. Hands-on experience with data analytics tools (SQL, SAS, Python, R) is an added advantage. Must Haves Technical Expertise: Deep understanding of fraud typologies in lending (application fraud, synthetic ID, collusion fraud, digital lending fraud, account takeover, etc.) Regulatory Knowledge: Comprehensive knowledge of Indian regulatory requirements (RBI Master Directions, KYC norms, Fair Practice Code, Fraud Classification norms) Analytics Proficiency: Hands-on experience with fraud analytics, rules engines, and transaction monitoring systems Investigation Skills: Proven track record in fraud investigation, evidence gathering, and case resolution Data Analysis: Proficiency in SQL and at least one statistical tool (SAS, Python, R) Should Haves Advanced Analytics: Experience with machine learning models for fraud detection Industry Exposure: Knowledge of digital lending ecosystems, credit bureaus, and fraud data consortiums Leadership Experience: Experience in managing fraud risk teams or cross-functional projects Technology Integration: Understanding of API integrations, real-time monitoring systems