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
What you will do: Understand the architecture and lead the development of our lending app across Android and iOS platforms. Own critical features end-to-end: from requirement analysis, design, coding, testing, deployment, to post-release monitoring. Build intuitive, high-performance, and secure app experiences that drive trust and adoption in financial products. Work closely with product, design, and backend teams to deliver features that improve loan journeys, repayments, and customer engagement. Integrate with native device capabilities, 3rd party SDKs, payment gateways, and regulatory frameworks (KYC, compliance, etc.). Optimize app performance for scale such as battery usage, network reliability, and offline-first workflows. Mentor junior developers, review code, and follow the best practices in mobile architecture and CI/CD pipelines. Bridge the native and cross-platform worlds by building and maintaining robust communication layers between our core Android components, ensuring seamless and performant interactions. Stay updated with industry trends (AI/ML-driven personalization, app security, open banking APIs) and bring innovative ideas to Prefr. You should apply if you: Have 5–8 years of proven experience in building and scaling consumer-facing apps (at least 1 end-to-end app on both Play Store & App Store). Are proficient in Kotlin/Java (Android) and Swift/Objective-C (iOS) , with good exposure to Flutter/React Native for cross-platform features. live and breathe performance tuning, with demonstrable experience in debugging and optimizing memory, threading, and rendering issues on Android Have experience in fintech or lending apps (preferred), understanding secure data handling, payments, and compliance requirements. Thrive on building pixel-perfect UI/UX and delivering smooth, reliable user journeys. Have strong problem-solving skills, an ownership mindset, and the ability to lead technical discussions. are open to learning new technologies and are excited to grow your skills across the mobile stack. Bonus: Experience with AI-first app development (AI-assisted workflows, personalization, or chatbot integration). Why join Prefr? Opportunity to shape the future of app-based lending in India . Work with a passionate, fast-moving team that values ownership and innovation. Flexible and transparent work culture. Competitive compensation and industry-best benefits. Must Have Demonstrated evidence of writing Kotlin (Android) and Swift (iOS) . Strong understanding of OOP and common design patterns ; practical MVVM on both platforms (Hilt/DI on Android; Coordinators/DI patterns on iOS). Strong analytical and troubleshooting skills for root-cause analysis (Crashlytics, Logcat/ADB, Xcode logs, Instruments/MetricKit). Solid networking fundamentals on both: Retrofit/OkHttp (Android) and URLSession/Alamofire (iOS); auth/refresh, interceptors/middlewares, robust error handling. Ability to build modular, extensible features/libraries (Android feature modules; iOS frameworks/SPM/CocoaPods). Demonstrated evidence of data/analytics instrumentation (Firebase Analytics/BigQuery or equivalent) and event taxonomy. Understanding of RESTful API usage and versioning; collaborate effectively with microservices backends. Deep links/app links on Android (HTTP App Links) and Universal Links on iOS; WebView/WKWebView integrations with JS bridges. Push notifications end to end: FCM (Android) and APNs (iOS), including deep links and attribution. Store operations & compliance on both: Google Play (targetSdk, Data Safety) and App Store (ATS/privacy, review guidelines). Should Have Foster usage of modern language constructs via code reviews (Coroutines/Flows, sealed classes on Android; async/await, structured concurrency on iOS). Experience with unit/UI/integration testing : JUnit/MockK/Espresso/Compose tests (Android); XCTest/XCUITest/Quick/Nimble (iOS). Understand high-level design and write low-level design docs for mobile features/modules. Manage and prioritize multiple tasks in a fast-paced environment; clear communication with product/QA/backend/design. Experience with the Firebase suite across both platforms: Analytics, Crashlytics, Remote Config, and Performance. Experience with CI/CD : Gradle + Fastlane/Play Publisher (Android); Xcode build systems + Fastlane/TestFlight (iOS); GitHub Actions/Jenkins. Experience with monitoring/release health : Crashlytics, Play Console vitals, Xcode Organizer/MetricKit. Nice to have Mobile security hardening: certificate pinning (OkHttp/TrustKit), Play Integrity (Android) / DeviceCheck/App Attest (iOS), anti-debugging/root/jailbreak heuristics. Ability to solve problems at scale (heavy WebView journeys, OEM/device fragmentation, performance & memory tuning). A/B experiments and Remote Config -driven rollouts.
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
What will you do? As a Data Scientist at Prefr, you will apply your creative problem-solving and analytical skills to design and deploy high-impact machine learning models across multiple business functions. You will be responsible for: Design, validate, and productionize advanced machine learning and deep learning models to generate actionable insights for strategic business decisions. Focus areas include credit risk prediction, propensity modeling, fraud detection, collection efficiency improvement, and other finance-related applications. Optimize and tune machine learning algorithms for performance and scalability, ensuring seamless integration with production pipelines and robustness in real-world environments. Develop and maintain monitoring frameworks to track model performance over time, detect data or concept drift, and provide timely, actionable feedback for retraining or recalibration as needed. Analyze and interpret large, complex datasets from distributed databases to generate and provide valuable actionable insights to stakeholders using big data technologies like Scala-Spark/PySpark. Drive continuous improvement by exploring, researching, and implementing innovative modeling techniques and algorithms. Stay up-to-date with advancements in ML/AI and proactively apply new techniques to improve model performance or uncover new opportunities. As part of our long-term vision, contribute to building agentic systems that automate key parts of the data science pipeline from feature engineering and model selection to monitoring and reporting, enabling faster experimentation and decision-making. You should apply if you Have 4+ years of hands-on experience (or 2+ years if holding a Master’s degree) in data science, machine learning, or analytics roles solving real-world business problems. Hold a Bachelor's or Master’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline. Are proficient in programming languages like Python or R, and comfortable working with large datasets and distributed computing tools. Have a strong foundation in machine learning algorithms, statistical modeling techniques, and data-driven decision-making. Excels at approaching complex problems with a structured mindset, driving data-backed and practical solutions. Communicate effectively and enjoy collaborating with cross-functional teams, including product, business, and engineering. Show strong business acumen, you don’t just build models, you build solutions that drive measurable impact. Are passionate about learning. You stay curious about new techniques, tools, and innovations in the AI space, and are excited to apply them to practical business use cases. Must have skills Model Development Experience: 4+ years of hands-on experience in building, validating, and deploying machine learning models in production environments (within fintech, lending, or related domains is a plus) Programming Expertise: Strong proficiency in Python, with practical experience using libraries like pandas, NumPy, scikit-learn, and frameworks such as XGBoost, LightGBM, or similar. Machine Learning & Statistics: Solid understanding and practical implementation experience of ML algorithms (e.g., logistic regression, random forest, gradient boosting, clustering) and statistical techniques (e.g., hypothesis testing, feature selection). Data Handling: Strong experience in handling large datasets and data pipelines; familiarity with SQL, distributed data environments, or PySpark is a plus. Problem Solving & Business Thinking: Ability to break down complex business problems into data science approaches and deliver actionable solutions with measurable impact. Collaboration & Communication: Proven ability to work in cross-functional teams alongside data engineers, business analysts, and product managers. Experience in articulating findings and recommendations clearly to non-technical stakeholders or leadership.
What will you do As a Data Scientist at Prefr, you will apply your creative problem-solving and analytical skills to design and deploy high-impact machine learning models across multiple business functions. You will be responsible for: Design, validate, and productionize advanced machine learning and deep learning models to generate actionable insights for strategic business decisions. Focus areas include credit risk prediction, propensity modeling, fraud detection, collection efficiency improvement, and other finance-related applications. Optimize and tune machine learning algorithms for performance and scalability, ensuring seamless integration with production pipelines and robustness in real-world environments. Develop and maintain monitoring frameworks to track model performance over time, detect data or concept drift, and provide timely, actionable feedback for retraining or recalibration as needed. Analyze and interpret large, complex datasets from distributed databases to generate and provide valuable actionable insights to stakeholders using big data technologies like Scala-Spark/PySpark. Drive continuous improvement by exploring, researching, and implementing innovative modeling techniques and algorithms. Stay up-to-date with advancements in ML/AI and proactively apply new techniques to improve model performance or uncover new opportunities. As part of our long-term vision, contribute to building agentic systems that automate key parts of the data science pipeline from feature engineering and model selection to monitoring and reporting, enabling faster experimentation and decision-making. You should apply if you Have 4+ years of hands-on experience (or 2+ years if holding a Master's degree) in data science, machine learning, or analytics roles solving real-world business problems. Hold a Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline. Are proficient in programming languages like Python or R, and comfortable working with large datasets and distributed computing tools. Have a strong foundation in machine learning algorithms, statistical modeling techniques, and data-driven decision-making. Excels at approaching complex problems with a structured mindset, driving data-backed and practical solutions. Communicate effectively and enjoy collaborating with cross-functional teams, including product, business, and engineering. Show strong business acumen, you don't just build models, you build solutions that drive measurable impact. Are passionate about learning. You stay curious about new techniques, tools, and innovations in the AI space, and are excited to apply them to practical business use cases. Must have skills Model Development Experience: 4+ years of hands-on experience in building, validating, and deploying machine learning models in production environments (within fintech, lending, or related domains is a plus) Programming Expertise: Strong proficiency in Python, with practical experience using libraries like pandas, NumPy, scikit-learn, and frameworks such as XGBoost, LightGBM, or similar. Machine Learning & Statistics: Solid understanding and practical implementation experience of ML algorithms (e.g., logistic regression, random forest, gradient boosting, clustering) and statistical techniques (e.g., hypothesis testing, feature selection). Data Handling: Strong experience in handling large datasets and data pipelines; familiarity with SQL, distributed data environments, or PySpark is a plus. Problem Solving & Business Thinking: Ability to break down complex business problems into data science approaches and deliver actionable solutions with measurable impact. Collaboration & Communication: Proven ability to work in cross-functional teams alongside data engineers, business analysts, and product managers. Experience in articulating findings and recommendations clearly to non-technical stakeholders or leadership.
What will you do? As a Data Scientist at Prefr, you will apply your creative problem-solving and analytical skills to design and deploy high-impact machine learning models across multiple business functions. You will be responsible for: Design, validate, and productionize advanced machine learning and deep learning models to generate actionable insights for strategic business decisions. Focus areas include credit risk prediction, propensity modeling, fraud detection, collection efficiency improvement, and other finance-related applications. Optimize and tune machine learning algorithms for performance and scalability, ensuring seamless integration with production pipelines and robustness in real-world environments. Develop and maintain monitoring frameworks to track model performance over time, detect data or concept drift, and provide timely, actionable feedback for retraining or recalibration as needed. Analyze and interpret large, complex datasets from distributed databases to generate and provide valuable actionable insights to stakeholders using big data technologies like Scala-Spark/PySpark. Drive continuous improvement by exploring, researching, and implementing innovative modeling techniques and algorithms. Stay up-to-date with advancements in ML/AI and proactively apply new techniques to improve model performance or uncover new opportunities. As part of our long-term vision, contribute to building agentic systems that automate key parts of the data science pipeline from feature engineering and model selection to monitoring and reporting, enabling faster experimentation and decision-making. You should apply if you Have 4+ years of hands-on experience (or 2+ years if holding a Master’s degree) in data science, machine learning, or analytics roles solving real-world business problems. Hold a Bachelor's or Master’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline. Are proficient in programming languages like Python or R, and comfortable working with large datasets and distributed computing tools. Have a strong foundation in machine learning algorithms, statistical modeling techniques, and data-driven decision-making. Excels at approaching complex problems with a structured mindset, driving data-backed and practical solutions. Communicate effectively and enjoy collaborating with cross-functional teams, including product, business, and engineering. Show strong business acumen, you don’t just build models, you build solutions that drive measurable impact. Are passionate about learning. You stay curious about new techniques, tools, and innovations in the AI space, and are excited to apply them to practical business use cases. Must have skills Model Development Experience: 4+ years of hands-on experience in building, validating, and deploying machine learning models in production environments (within fintech, lending, or related domains is a plus) Programming Expertise: Strong proficiency in Python, with practical experience using libraries like pandas, NumPy, scikit-learn, and frameworks such as XGBoost, LightGBM, or similar. Machine Learning & Statistics: Solid understanding and practical implementation experience of ML algorithms (e.g., logistic regression, random forest, gradient boosting, clustering) and statistical techniques (e.g., hypothesis testing, feature selection). Data Handling: Strong experience in handling large datasets and data pipelines; familiarity with SQL, distributed data environments, or PySpark is a plus. Problem Solving & Business Thinking: Ability to break down complex business problems into data science approaches and deliver actionable solutions with measurable impact. Collaboration & Communication: Proven ability to work in cross-functional teams alongside data engineers, business analysts, and product managers. Experience in articulating findings and recommendations clearly to non-technical stakeholders or leadership.
As a Decision Scientist, you will be the analytical backbone supporting Business, Growth, Product, and Risk teams, transforming complex data into strategic insights that drive multi-functional success across our lending ecosystem. Analyze multi-dimensional data to identify user patterns, emerging trends, and risk signals that inform lending and growth strategies. Develop and maintain churn and propensity models to detect disengaged users and recommend data-driven interventions that improve retention and product stickiness. Lead campaign analytics by building user segments and evaluating communication effectiveness, with a focus on improving targeting, engagement, and in-funnel conversion performance. Partner with product teams to guide feature development, using insights from cohort analysis, funnel diagnostics, and retention trends to improve user journeys and experience. Collaborate with cross-functional teams including Product, Risk, Tech, Compliance, and Business to build and execute analytical solutions aligned with organizational priorities. Own the end-to-end execution of strategic analytical initiatives, from problem framing and hypothesis design to stakeholder communication and impact tracking. Support and mentor junior analysts, fostering analytical rigor and promoting best practices in data hygiene, modeling, and experimentation across the broader analytics team. You should apply if you Have 4+ years of hands-on experience in data science or analytics roles, solving real-world business problems. Hold a Bachelor's or Master’s degree in a quantitative field such as Engineering, Statistics, Mathematics, or a related discipline. Have strong analytical skills with the ability to interpret diverse datasets and translate insights into solutions for specific business challenges. Have strong proficiency in Python and/or SQL, with experience handling large datasets and working with distributed computing tools. Communicate effectively and enjoy collaborating with cross-functional teams, including product, business, and risk. Are passionate about data, always eager to explore, experiment, and apply data-driven approaches to solve complex problems. Must Have Skills Strong proficiency in Python programming and SQL querying, with experience handling and analyzing large datasets. Excellent quantitative and analytical skills with the ability to translate data insights into actionable business solutions. Strong organizational, communication, and presentation skills to effectively collaborate with cross-functional teams and stakeholders. Proven experience driving business impact through analytical projects from end-to-end. Passion for working with data and solving complex problems in a fast-paced environment.
What you will do? Understand the architecture and lead the development of our lending app across Android and iOS platforms. Own critical features end-to-end: from requirement analysis, design, coding, testing, deployment, to post-release monitoring. Build intuitive, high-performance, and secure app experiences that drive trust and adoption in financial products. Work closely with product, design, and backend teams to deliver features that improve loan journeys, repayments, and customer engagement. Integrate with native device capabilities, 3rd party SDKs, payment gateways, and regulatory frameworks (KYC, compliance, etc.). Optimize app performance for scale such as battery usage, network reliability, and offline-first workflows. Mentor junior developers, review code, and follow the best practices in mobile architecture and CI/CD pipelines. Bridge the native and cross-platform worlds by building and maintaining robust communication layers between our core Android components, ensuring seamless and performant interactions. Stay updated with industry trends (AI/ML-driven personalization, app security, open banking APIs) and bring innovative ideas to Prefr. You should apply if you: Have 5–8 years of proven experience in building and scaling consumer-facing apps (at least 1 end-to-end app on both Play Store & App Store). Are proficient in Kotlin/Java (Android) and Swift/Objective-C (iOS) , with good exposure to Flutter/React Native for cross-platform features. live and breathe performance tuning, with demonstrable experience in debugging and optimizing memory, threading, and rendering issues on Android Have experience in fintech or lending apps (preferred), understanding secure data handling, payments, and compliance requirements. Thrive on building pixel-perfect UI/UX and delivering smooth, reliable user journeys. Have strong problem-solving skills, an ownership mindset, and the ability to lead technical discussions. are open to learning new technologies and are excited to grow your skills across the mobile stack. Bonus: Experience with AI-first app development (AI-assisted workflows, personalization, or chatbot integration). Why join Prefr? Opportunity to shape the future of app-based lending in India . Work with a passionate, fast-moving team that values ownership and innovation. Flexible and transparent work culture. Competitive compensation and industry-best benefits. Must Have Demonstrated evidence of writing Kotlin (Android) and Swift (iOS) . Strong understanding of OOP and common design patterns ; practical MVVM on both platforms (Hilt/DI on Android; Coordinators/DI patterns on iOS). Strong analytical and troubleshooting skills for root-cause analysis (Crashlytics, Logcat/ADB, Xcode logs, Instruments/MetricKit). Solid networking fundamentals on both: Retrofit/OkHttp (Android) and URLSession/Alamofire (iOS); auth/refresh, interceptors/middlewares, robust error handling. Ability to build modular, extensible features/libraries (Android feature modules; iOS frameworks/SPM/CocoaPods). Demonstrated evidence of data/analytics instrumentation (Firebase Analytics/BigQuery or equivalent) and event taxonomy. Understanding of RESTful API usage and versioning; collaborate effectively with microservices backends. Deep links/app links on Android (HTTP App Links) and Universal Links on iOS; WebView/WKWebView integrations with JS bridges. Push notifications end to end: FCM (Android) and APNs (iOS), including deep links and attribution. Store operations & compliance on both: Google Play (targetSdk, Data Safety) and App Store (ATS/privacy, review guidelines).