Senior Research Engineer - Applied AI/ML at ixigo

Gurugram, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

14 Jul, 26

Salary

0.0

Posted On

15 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, SciPy, SQL, Airflow, Spark, Kafka, Dbt, Machine Learning, Data Engineering, Statistical Analysis, A/B Testing

Industry

technology;Information and Internet

Description

Company Description Launched in 2007 by Aloke Bajpai & Rajnish Kumar, ixigo is a technology company focused on empowering Indian travelers to plan, book and manage their trips across rail, air, buses and hotels. ixigo assists travelers in making smarter travel decisions by leveraging artificial intelligence, machine learning and data science-led innovations on ixigo’s OTA platforms, including websites and mobile applications. AbhiBus, part of the Ixigo group, is India’s leading online bus ticketing platform. Founded in 2007, Abhibus connects millions of travellers with 5000+ private operators, 18+ State Transport Corporations. Alongside our consumer platform, we power the bus industry with SaaS solutions across e-ticketing, fleet management, vehicle tracking, and passenger information systems. Ixigo group overall serves 80+ million travellers across flights, trains, buses, hotels & holidays. Our mission is simple: to simplify the lives of travellers through intelligent, AI- driven travel products. Job Description We are looking for a Data Science Engineer / Senior Research Engineer to join our applied AI team at Abhibus. This is a hybrid role at the intersection of AI/ML, classical data science, and data engineering. You’ll be responsible for: Designing and building AI-driven features that power search, personalization, pricing, recommendations, and fraud detection. Developing robust data pipelines and scalable infrastructure to ensure reliable ML model training and deployment. Generating statistical and business insights from large-scale bus travel data to shape product strategy. Your work will touch millions of travellers and hundreds of bus operators, bringing data- driven innovation to the mobility ecosystem Key Responsibilities: Design and implement ML models for personalization, demand forecasting, route optimization, pricing, anomaly detection, and recommendations. Build, maintain, and optimize ETL/data pipelines to feed ML models and analytics dashboards. Work with product managers & engineers to translate business requirements →models → production systems. Perform statistical analysis, A/B testing, and causal inference for product & growth experiments. Read and adapt academic research papers into practical product applications. Ensure model lifecycle management: data collection, feature engineering, training, deployment, monitoring, and retraining. Collaborate with data engineering team on data architecture, warehousing, and scaling pipelines. Qualifications Background in Computer Science, Engineering, or Mathematics (top institutes preferred). 2–6 years of experience in data science & engineering. Strong fundamentals in algorithms, data structures, ML/DL, and statistics. Proficiency in Python and libraries like TensorFlow/PyTorch, Scikit-learn, Pandas, NumPy, SciPy. Hands-on experience with SQL and data pipelines (Airflow, Spark, Kafka, dbt, or equivalent). Ability to translate ambiguous problems into structured models and scalable solutions Additional Information Preferred Skills: Experience with cloud platforms (AWS, GCP, Azure) and ML deployment tools (SageMaker, MLflow, Kubeflow). Knowledge of big data technologies (Spark, Hadoop, Presto, ClickHouse). Experience with travel, mobility, or marketplace problems. Contributions to open-source ML or published research Candidates are responsible for safeguarding sensitive company data against unauthorized access, use, or disclosure, and for reporting any suspected security incidents in line with the organization's ISMS (Information Security Management System) policies and procedures.

Responsibilities

Design and implement machine learning models for personalization, demand forecasting, and route optimization. Build and maintain robust data pipelines while collaborating with product managers to translate business requirements into production systems.