Machine Learning Engineer

Machine Learning Engineer
A US-headquartered product company scaling its India hub
Location: Remote (India)
About the Company
A US-headquartered product company with a large, established customer base is scaling its India engineering hub and has partnered with Flexiple to hire a Machine Learning Engineer for its core platform team. The team owns ML features that already run in production at scale and need an engineer who can extend and harden them. Flexiple is a managed marketplace that helps global companies build high-performing teams and GCCs in India.
Role Overview
As a Machine Learning Engineer, you will build, evaluate, and ship models into the company's production product, working closely with backend and platform engineers to keep inference reliable at scale.
Key Responsibilities
Model Development
Design, train, and evaluate ML models for ranking, recommendation, or NLP use cases within the product
Run structured experiments and A/B tests to validate model impact against product metrics
Iterate on features, labels, and model architecture based on production feedback
Production & Platform
Build and maintain training and inference pipelines that run reliably at scale
Own model deployment, versioning, monitoring, and rollback in production
Partner with backend engineers to integrate models into existing services with low latency
Ideal Candidate Profile
2 to 6 years building and shipping ML models into production systems
Strong Python skills and hands-on experience with PyTorch or TensorFlow
Comfortable with the full lifecycle from data pipelines to deployed, monitored models
Clear written and spoken English and a reliable remote-work setup
Preferred Qualifications
Experience with MLOps tooling such as MLflow, Airflow, or Kubeflow
Exposure to large-scale data systems (Spark, feature stores, or streaming pipelines)
A degree in computer science, statistics, or a related quantitative field
What We Offer
A core ML seat embedded in a US product company's India engineering hub
Remote-friendly, pan-India hiring with long-term placement
Direct exposure to production-scale ML systems and senior engineering leadership
One application considered for this and future matched roles
Hiring Process
HR Screening → Technical Assessment → System Design Round → Culture Fit

Work arrangement
No