Director - Technology (Personalization)

Job Title : Director — Technology Development (Personalization)Location : BengaluruExperience : 15+ YearsFunction : Data Platform & PersonalizationWhy This RoleReimagine travel personalization at platform scale across Flights, Hotels, Holidays, Trains, and Activities.Own end-to-end personalization charter : engineering, data science, MLOps, and experimentation.Collaborate with ContextOS, Myra, and Experience Hub under the “One Brain, Many Adapters” vision.What You’ll OwnStrategy & Roadmap : Drive 12–18 months personalization roadmap aligned to KPIs (growth, conversion, retention, experience).Platforms, Not Projects : Build reusable ranking / reco / decisioning platforms (feature / embedding store, real-time signals, policy / constraints, evaluation).ML in Production : Lead model lifecycle (user modeling, content understanding, candidate generation, re-ranking, optimization, bandits / RL) with clear SLOs.Experimentation & Causality : Scale A / B and bandit experimentation; ensure robust guardrails and uplift measurement.Privacy-First Personalization : Embed consent, PII handling, and privacy-by-design in data contracts; ensure regulatory compliance.Agentic & Real-Time Experiences : Integrate with ContextOS and Myra for explainable, grounded, actionable recommendations across channels.People & Org : Lead a 25–40 members team across Backend / Platform engineers, MLEs, DS / Applied Scientists, and Program; hire, coach, and build succession.Partnering & Influence : Collaborate with Product, Design, Marketing, Supply, Finance, and Legal; manage vendors / partners.Observability & Evals : Establish evaluation frameworks and operational excellence (SRE, On-call, RCA).Cost & Reliability : Own budgets, infra choices, and efficiency (GPU / CPU mix, caching, quantization, distillation).What You’ve Done15+ years in consumer tech / platforms with 7+ years leading cross-functional Eng + DS / MLE teams.Delivered production ML (recommendations / ranking / search / ads / feeds) for millions of users with measurable uplift and strong SLOs.Built / owned feature & embedding stores, online feature serving, streaming pipelines, and A / B platforms.Deep hands-on expertise in large-scale backend (Java / Scala / Go) or applied ML (Python, PyTorch / TensorFlow) with architectural leadership.Experience with real-time systems (Kafka / PubSub, Flink / Spark Streaming), datastores (Redis, Cassandra / DynamoDB, Elasticsearch), MLOps (MLflow / Kube / Triton / Ray), and cloud (AWS / Azure / GCP).Proven implementation of privacy-by-design, data contracts, and governance in production pipelines.Strong product sense and storytelling; ability to align executives and mentor senior ICs and managers.Nice to HaveExperience with multi-objective optimization, constrained ranking, or contextual bandits / RL.Familiarity with knowledge graphs / ontologies, retrieval / RAG, agentic workflows, LangGraph / LangChain, and LLM / agent evaluation frameworks.Experience in Indian consumer internet scale, marketplaces, or travel.