About the Role

Build, train, deploy, and operate the ML engine powering TourIQ, a B2B dynamic pricing platform for the tours & activities industry. You’ll work across traditional ML, LLM integration, and full MLOps — no separate MLOps hire, you own the complete lifecycle from development through production. Your models directly impact customer revenue.

Key Responsibilities

ML Pricing Engine

• Design, train, and deploy ML models for price optimization using diverse real-world signals
• Build ensemble approaches combining multiple model outputs with confidence scoring
• Engineer and maintain a large feature set spanning temporal, environmental, demand, and competitive signals
• Handle scenarios with limited historical data for new customers
• Implement guardrails and fallback logic for low-confidence predictions
• Train and manage ML models for diverse customer segments at scale

MLOps & Production Operations

• Experiment tracking and model versioning; production model serving
• Build automated retraining pipelines with scheduled and performance-triggered cycles
• Model monitoring, drift detection, and alerting in production
• Model rollback and recovery procedures
• Explainability: surface key factors behind each recommendation so users understand and trust outputs
• Ensure consistency between training and serving data

AI Assistant & LLM Integration

• Build an AI assistant using LLM APIs for natural language interaction with platform data
• Implement RAG for contextual retrieval and knowledge grounding
• AI agent workflows: query data, generate reports, explain decisions in plain language

Collaboration

• Partner with Data Engineer, Tech Lead, and UI/UX Designer across the product

Must-Have

• 3+ years applied ML/Data Science with production models (not just notebooks/Kaggle)
• Python with PyTorch and XGBoost/scikit-learn
• End-to-end ML pipelines: data prep, feature engineering, training, evaluation, deployment, monitoring
• MLOps: automated retraining, model monitoring/drift detection, versioning and rollback in production
• Regression models, ensemble methods, gradient boosting, time-series forecasting
• Production model serving experience
• Experiment tracking (MLflow, W&B, or similar); SQL/PostgreSQL for feature engineering
• LLM API integration (OpenAI, Anthropic/Claude, or similar) in production

Good to Have

• Dynamic pricing / revenue management / demand forecasting; LSTM/RNN; Reinforcement Learning; RAG; vector databases; A/B testing; CI/CD for ML models; PySpark

Mindset

Ships production models AND keeps them running. Full lifecycle ownership. Starts simple, scales with data. Revenue-driven. Explains ML to non-technical users. Startup mentality.

Why Join

Own the entire ML/AI stack from day one. Build models that drive measurable revenue lift. Traditional ML + LLM + MLOps. Onsite in Jaipur.