Job DescriptionLead a team of HW designers to design and develop hardware components for battery management systems (LV and HV)Experience in defining project scope, timeline, budget, and resource allocation across hardware development projectsExperience in Functional Safety topics for HardwareConducting requirement analysis and handling in DOORSPCN / PTN know-how and process handledDesigning electronic circuits, creating creepage distance matrices, and reviewing PCB layoutsSelecting components using DAR and creating requirements and test specificationsDocumenting development processes and results (e.g., Module design document preparation, Test plan preparation, performing testing, Test report preparation)Performing FMEA and FMEDA for the developed modules’ Requirement analysis / studyPerforming Monte carlo simulations, worst case calculations, and tests to ensure functionality and qualityPerforming HW Verification and supporting validation (LV124/ ISO 16750)DRBFM or High Voltage testing experience is added advantageSkillsFunctional Safety ExpertiseRequirement analysisHandling of lab equipment like Oscilloscope, high voltage power supply, function generator etc.,Good knowledge on Analog, Digital & Power Electronics circuits designExcellent debugging skillsGood communication skills,MS office applicationsHands on soldering experience
Define the long-term AI roadmap: You won’t just follow trends; you’ll decide how Machine Learning and AI can uniquely solve travel pain points.
Build & Scale: Lead a world-class team of Applied Scientists and ML Engineers.
Consumer Impact: Deeply understand consumer behavior to build models that feel like magic to the user.
Cross-Pollination: Partner with Product and Engineering to ensure AI isn't a silo, but a horizontal layer that enhances every click on the ixigo, ConfirmTkt, and AbhiBus apps.
Your DNA
The Scientist-Builder: You have a PhD or Master's in CS/AI/Math, but you also have "calluses on your hands" from shipping real products. You know that a model is only as good as the problem it solves.
LLM Native: You live and breathe Transformers. You understand the nuances of prompt engineering, fine-tuning, and the infrastructure required to serve large models at scale.
The Scale Expert: You’ve handled datasets that would make a regular laptop melt. You are comfortable with distributed computing, MLOps, and optimizing models for mobile-first environments.
Strategic & Witty: You can explain complex neural networks to a boardroom and the business value of a gradient descent to a developer—and you might even make a joke about it in the process.
Technical Requirements
1. Foundation Models & Generative AI
Deep expertise in traditional and foundational Machine learning techniques and models.
Extensive experience with Deep learning architectures, fine-tuning, and efficient adaptation techniques.
Proven ability to build autonomous agents capable of multi-step reasoning and tool-based execution.
Expertise in building and deploying high-concurrency models at scale.
Mastery of production-grade retrieval systems and vector database management.
Experience with Vision-Language Models for automating document parsing and voice-interface models for vernacular language support.
2. Architecture & MLOps
Experience with high-throughput, low-latency serving frameworks.
Ability to architect AI-first microservices that handle hundreds of millions of requests monthly while managing compute costs.
3. Academic & Professional Pedigree
Education: MS or PhD in Computer Science, AI, Mathematics, or a related quantitative field.
Experience: 10+ years in technology, with at least 5+ years leading high-performance AI research or applied science teams in a fast-paced consumer tech environment.
The "Builder" Portfolio: A portfolio of shipped products is preferred over a portfolio of just research papers. We value "Production-ready Research.