1015 years

Founding AI Leadership Cross-Platform Intelligence LLM Systems Voice AI Agentic Commerce

The Mission :

Most companies hire an AI team to build features.

We are hiring a Head of AI/ML to define the intelligence architecture of a billion-dollar travel marketplace from the ranking algorithm that determines which flight appears first, to the voice interface that lets a first-generation smartphone user book a train ticket by speaking in Bhojpuri.

The Head of AI/ML owns the AI brain of India's most ambitious travel platform.

You define what problems ML can solve, design the systems to solve them, hire the team that builds them, and ensure that every model in production is measurably improving the lives of the users it touches.

This is a role for someone who has done this before at scale, under pressure, with real consequences.

What You Will Build & Own :

1. AI Platform & Infrastructure

- Design and own the end-to-end ML platform: feature store, model training pipelines, experiment tracking, real-time serving infrastructure, and model lifecycle management

- Build the AI observability layer: model drift detection, prediction confidence monitoring, A/B experiment guardrails, and business metric alignment dashboards

- Architect the LLM infrastructure: fine-tuning pipelines (LoRA/QLoRA), RLHF workflows, RAG vector stores (Pinecone/Weaviate), and prompt versioning systems

- Own Voice AI architecture: ASR pipelines for Hindi/Hinglish/regional languages, TTS output quality, intent-to-action mapping for spoken booking flows

- Define and govern MCP (Model Context Protocol) orchestration standards for all agentic systems across verticals

2. Cross-Platform Intelligence Layer :

- Search & Ranking: Multi-stage Learning-to-Rank (L2R) across flights, hotels, bus, trains blending relevance signals, conversion probability, commercial objectives, and real-time inventory

- Personalisation Engine: Cross-vertical user embeddings, real-time context-aware recommendations, session-based deep learning models (BERT4Rec, SASRec), cold-start resolution

- Price Intelligence: Demand forecasting (LSTM/Transformer), price elasticity modelling, competitive fare mapping, lower-price guarantee trigger systems, yield optimisation

- Recommendation Systems: Collaborative filtering (neural CF), content-based for new inventory, hybrid systems, cross-sell/upsell next-best-action models

- Coupon & Promotional ML: Personalised offer targeting, redemption probability models, margin-aware discount optimisation, cannibalization detection

- Sentiment Intelligence: Multi-source review aggregation, LLM-powered summarisation, NPS prediction, complaint intent routing, fake review detection

- Fraud & Trust: Real-time transaction anomaly scoring, account takeover detection, payment fraud ML, bot traffic classification

3. Generative AI & Agentic Systems :

- Conversational Travel Planning : LLM-powered trip intent understanding, itinerary generation, multi-turn booking dialogues, context-aware fare recommendations

- RAG Pipelines : Fare rule knowledge bases, hotel cancellation policy retrieval, airline contract intelligence, IRCTC regulation Q&A, refund policy automation

- Agentic Workflows : Autonomous booking exception handling, supplier communication agents, refund orchestration bots, post-booking modification agents

- Voice AI Product : End-to-end voice booking flows in Hindi/Hinglish, spoken fare comparison, accessibility-first voice navigation for feature phone users

- MCP Tool Chains : Structured tool-use orchestration across GDS APIs, payment gateways, supplier systems, and internal microservices

4. Org & Culture :

- Hire and lead a team of 1525 Data Scientists, ML Engineers, Research Scientists, and AI Product Managers

- Embed AI engineers into each product vertical (Flights, Hotels, Bus, Train, B2B, Core) while maintaining central platform ownership

- Build an experimentation culture: every hypothesis gets a test, every test gets an evaluation framework, every evaluation gets a business interpretation

- Partner with Director-level Engineering, Product, and Business leaders to define the AI roadmap and prioritise investments

Technical Depth Required :

- Real-time ML serving: model inference in the critical booking path at p99 < 20ms with GPU autoscaling

- LLM systems: fine-tuning (LoRA/QLoRA/full), RLHF, DPO, RAG architecture, hallucination mitigation, and evaluation frameworks

- Ranking systems: multi-objective L2R (LambdaMART, Neural IR), explore-exploit trade-offs, position bias correction

- Causal ML: A/B design, uplift modelling, counterfactual evaluation, CUPED variance reduction

- Voice AI: ASR/TTS pipeline design, WER optimisation for Indian languages, intent classification for spoken input

- Recommendation systems: matrix factorisation, neural collaborative filtering, session-based models, multi-armed bandits

- Feature engineering at scale: streaming features (Flink/Kafka Streams), feature drift, backfill pipelines

- Agentic system design: ReAct patterns, tool-use orchestration, chain-of-thought reliability, hallucination guardrails

The AI-First Mandate:

AI is not an enhancement.

It is the product architecture.

Every surface, every API, every decision point is either ML-powered today or on the roadmap to be.

- Search & Ranking Learning-to-Rank across flights, hotels, bus routes, train coaches; real-time re-ranking on user signals

- Voice AI Hindi/Hinglish voice booking, intent resolution, spoken fare comparisons, accessibility-first conversational UX

- RAG Systems Fare rule retrieval, hotel cancellation policy Q&A, airline contract intelligence, real-time regulatory updates

- Agentic AI Autonomous booking resolution, exception handling, refund orchestration, supplier communication bots

- MCP Orchestration Model Context Protocol tool chains across GDS APIs, payment gateways, and supplier integrations

- Recommendation Engine Cross-vertical next-best-action, collaborative filtering, session-based deep learning

- Price Intelligence Competitive fare mapping, lower-price guarantee engine, demand elasticity, yield optimisation

- Coupon & Promo ML Personalised offer targeting, redemption probability scoring, margin-aware discount optimisation

- Sentiment & Review AI Review summarisation, NPS prediction, complaint triage, trust signal extraction

- Fraud & Risk ML Anomaly detection, account takeover signals, payment fraud scoring, fake review classification

- Deep System Mapping Route intelligence, geo-semantic matching, multi-modal journey planning

- Predictive Systems Cancellation risk, no-show prediction, seat upgrade probability, waitlist conversion

Infrastructure & Scale Context :

The systems you will work on or depend on :

Compute

- AWS EKS (Kubernetes) 500+ pods, autoscaling, spot instance optimisation

Cache

- Multi-tier: L1 in-process (Caffeine) ? L2 Redis Cluster ? L3 CDN edge cache; 10M+ keys/sec

Streaming

- Apache Kafka 100+ topics, 5M+ events/sec, consumer lag SLOs < 500ms

Storage

- Polyglot: DynamoDB (booking state) Aerospike (fare cache) Elasticsearch (search) Aurora (OLTP)

Observability

- OpenTelemetry traces ? Jaeger; Prometheus metrics ? Grafana; structured logs ? Loki; SLO dashboards

ML Serving

- Real-time: TorchServe / Triton on GPU nodes, p99 < 20ms.

- Batch: Spark on EMR

Feature Store

- Feast 300+ features, online (Redis) + offline (S3/Hive), sub-10ms online reads

APIs

- REST + gRPC; 200+ internal services; 50M+ external API calls/day; contract testing via Pact

Security

- OAuth2/JWT, Vault for secrets, AWS IAM, zero-trust internal service mesh

Who You Are:

- 1015 years in ML/AI with at least 35 years leading an ML org or founding an AI function at a high-growth company

- Deep hands-on expertise across ranking, NLP, LLMs, and recommendation systems you can review and improve any model your team builds

- Track record of shipping production AI systems that moved business metrics at scale not just model benchmarks

- Experience with LLM fine-tuning, RLHF pipelines, RAG architecture, and agentic system design in production

- Comfort operating at both technical depth (architecture review, model design) and organisational breadth (hiring, strategy, cross-functional alignment)

- Experience with Voice AI or multilingual NLP is strongly preferred

- Tier-I institute background strongly preferred (IIT / IIIT / IISC / BITS / IIMs for business-facing components)

Technology Stack :

ML Frameworks : PyTorch TensorFlow JAX XGBoost LightGBM Scikit-learn

LLM / GenAI : LangChain LlamaIndex vLLM HuggingFace PEFT Instructor DSPy

Voice AI : Whisper IndicASR Coqui TTS Bhashini APIs Speechbrain

RAG / Vector : Pinecone Weaviate FAISS pgvector Qdrant

ML Platform : MLflow Weights & Biases Feast Ray Kubeflow Airflow

Data & Streaming : Spark Flink Kafka dbt BigQuery Hive Delta Lake

Serving Infra : Triton TorchServe BentoML FastAPI AWS SageMaker GPU nodes

Why This Role Is Different :

- You are the first person at this company to define what AI means at this scale the decisions you make in year one will still be running in year five.

- The data richness is extraordinary: 5 verticals 100M users real transaction outcomes = training signal that most ML teams never accumulate.

- Voice AI for Bharat is an unsolved frontier building spoken booking flows in 20+ Indian languages is genuinely hard and genuinely important.

- The impact is visible and human: a better recommendation means a family finds a cheaper flight home. A better fraud model means a small business's corporate account isn't drained.

- You will not be optimising incremental metrics. You will be defining the intelligence of a platform that becomes foundational infrastructure for Indian travel