Machine Learning Engineer

Overview
Eighty Days is a Mumbai-based travel technology startup reimagining how people discover, plan, and experience travel. Our platform simplifies trip planning by helping travelers uncover hidden gems and organize unique experiences into personalized journeys. With over 100K+ downloads and 20,000 vacations already planned, we’re building the future of travel discovery.

Responsibilities
Design, develop, and deploy  supervised and unsupervised machine learning models  to generate user personas from large-scale behavioral and user interaction data.
Develop, enhance, and maintain  recommendation systems  to personalize destinations, itineraries, and content for users.
Train, validate, evaluate, and monitor machine learning models to ensure strong  performance, scalability, and accuracy  in production environments.
Build and manage efficient  ETL data pipelines  to support information retrieval and multiple downstream applications.
Document model experiments, evaluations, and performance metrics using  Weights & Biases (W&B).
Work with AWS services such as  EC2, S3, and Load Balancers  to deploy and scale machine learning systems.
Stay up to date with advancements in machine learning, recommender systems, and applied AI, and incorporate relevant innovations into production systems.
Design and implement  agentic workflows using Lang Graph , and develop information retrieval systems leveraging  vector databases  for RAG use cases.
Work with large embedding models for information retrieval, including an understanding of fine-tuning techniques.
Build and maintain similarity-based retrieval systems using vector databases and No

SQL technologies such as  Mongo

DB  and  Elasticsearch .
Integrate  Model Context Protocol (MCP)  with agentic systems to enable scalable and modular AI workflows.
Collaborate with project manager, data analyst, and backend engineers to translate business requirements into effective machine learning solutions.

Requirements
Minimum 3–5 years  of hands-on experience in core machine learning or applied AI roles
Demonstrated experience building recommendation systems, personalization engines, or other user-facing ML features
Strong understanding of machine learning fundamentals, including  supervised and unsupervised  learning, feature engineering, and building training datasets for model development
Solid foundation in  probability, statistics, and algorithms
Proficiency in Python with experience using ML frameworks such as  PyTorch and Torch Serve
Experience working with large datasets and production-grade ML pipelines
Familiarity with data modeling, data structures, and software engineering best practices
Ability to translate ambiguous business problems into clearly defined ML objectives
Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related field (or equivalent practical experience)
Preferred working in a fast-paced AI-driven startup environment with a high degree of flexibility