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Roles & Responsibilities: - Lead AI Product Pods across Credit Risk, Fraud, and Collections functions. - Build and deploy production-scale Machine Learning systems for lending lifecycle decisioning. - Own complete ML lifecycle including feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement. - Design scalable distributed ML infrastructure, feature stores, model registries, and MLOps pipelines. - Develop AI solutions for underwriting, portfolio risk monitoring, fraud detection, anomaly detection, and recovery optimization. - Drive model governance, monitoring, explainability, and compliance within BFSI regulatory standards. - Collaborate with Product, Risk, Engineering, Data, and Business teams to deliver AI-driven business outcomes. - Define AI platform architecture, operational excellence, SLAs, and incident management practices. - Build, mentor, and scale high-performing AI Engineering and Data Science teams. Ideal Candidate: - 1. Strong Lead Data Science, AI Engineer, or Machine Learning Engineer profiles. - 2. Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems. - 3. Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions. - 4. Mandatory (Experience 3) - Candidate's Current designation must be Lead or above. - 5. Mandatory (Experience 4) - Must have solid experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment. - 6. Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing. - 7. Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting. - 8. Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms. - 9. Mandatory (Experience 8) - Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices. - 10. Mandatory (Education) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered. - 11. Mandatory (Age) - Candidate's Age should be below 37 years. - 12. Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy. - 13. Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred. - 14. Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply. - 15. Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure. Interview Process: - 3 Technical Rounds - Underwriting Call - Criminal Check - HR Discussion Perks & Benefits: Our people define our passion and our audacious, incredibly rewarding achievements. Bajaj Finance Limited is one of Indias most diversified Non-banking financial companies, and among Asias top 10 Large workplaces. If you have the drive to get ahead, we can help find you an opportunity at any of the 500+ locations were present in India. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.