ML​/AI Engineer

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/Postgre

SQL 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;
Py Spark

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.