Production ML Engineer - Build & Deploy Scalable AI

Talemexa Pvt Ltd; San Francisco (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Talemexa Private Limited is seeking a Machine Learning Engineer to build, deploy, and maintain production-grade ML systems. You will work across the ML lifecycle—from data prep and experimentation to model deployment and monitoring, with a focus on responsible AI practices.

The role involves collaborating with data scientists, infra engineers, and product teams to improve inference performance and scalability in production environments. 3–6 years experience and strong Python/SQL are required.

Qualifications

• 3–6 years of experience in machine learning engineering.
• Strong proficiency in Python and SQL.
• Experience with PyTorch, TensorFlow, or Scikit-learn.
• Experience with cloud infrastructure, model deployment, and MLOps workflows.
• Understanding of statistical modeling, algorithms, and model evaluation.

Responsibilities

• Design and develop machine learning models for prediction, personalization, ranking, and recommendation.
• Build data preparation and feature engineering pipelines.
• Train, evaluate, and optimize models using suitable algorithms and frameworks.
• Deploy machine learning models into production environments.
• Develop monitoring and retraining workflows to maintain model quality.
• Improve inference performance, scalability, and computational efficiency.
• Collaborate with data scientists, infrastructure engineers, and product teams.
• Maintain technical documentation and follow responsible AI practices.

Skills

Python

SQL

MLOps

Model Deployment

Feature Engineering

Education

Bachelor's or Master's in CS/ML/Math or equivalent

Tools

PyTorch

TensorFlow

Scikit-learn

Docker

Cloud Computing

Job description

Talemexa Private Limited is seeking a Machine Learning Engineer to build, deploy, and maintain production-grade ML systems. You will work across the ML lifecycle—from data prep and experimentation to model deployment and monitoring, with a focus on responsible AI practices.

The role involves collaborating with data scientists, infra engineers, and product teams to improve inference performance and scalability in production environments. 3–6 years experience and strong Python/SQL are required.