Director of Technology Development

Job Title: Director Technology Development (Personalization)

Location: Bengaluru

Experience: 15+ Years

Function: Data Platform & Personalization

Why This Role

• Reimagine 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 Own

• Strategy & Roadmap: Drive 1218 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 2540 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 Done

• 15+ 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 Have

• Experience 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.