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31 ClanX Jobs

Machine Learning Engineer

ClanX

5-10 years

Bangalore / Bengaluru

1 vacancy

ClanX

posted 3hr ago

Job Role Insights

Key skills for the job

Machine Learning Tensorflow Pytorch

Job Description

Overview

RadiusAI is hiring a Senior Machine Learning Engineer to design, develop, and deploy advanced AI models, focusing on innovation in machine learning, computer vision, and NLP.

Requirements

Minimum of 5 years of professional experience in software engineering with a strong focus on AI/ML model development and deployment.
Proven expertise in developing and deploying machine learning models, particularly deep learning models using PyTorch (preferred) or similar frameworks.
Expertise in optimizing AI models for performance and scalability, including techniques such as pruning, quantization, and distillation.
Hands-on experience with MLOps tools such as Docker, Kubernetes, MLflow, TensorBoard, and CI/CD pipelines.
Familiarity with cloud platforms such as AWS, GCP, or Azure and their respective machine learning services (e.g., SageMaker, AI Platform).
Strong proficiency in Python for machine learning and AI model development.
Solid understanding of state-of-the-art machine learning techniques, including:
- Neural Networks (CNNs, RNNs, Transformers)
- Reinforcement Learning
- Natural Language Processing (NLP)
- Computer Vision
Strong understanding of data structures, algorithms, and performance optimization principles.
Bachelor's degree in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience). A Masters degree is a plus.

Responsibilities

Design, develop, and deploy advanced AI models (including deep learning, computer vision, and NLP) using frameworks like PyTorch.
Optimize models for performance and scalability using state-of-the-art techniques such as quantization, pruning, and knowledge distillation.
Build and maintain robust, scalable machine learning pipelines using MLOps best practices and tools like Docker, Kubernetes, and MLflow.
Collaborate with cross-functional teams (data scientists, software engineers, product managers) to integrate AI models into scalable production systems.
Lead efforts in data preprocessing, cleaning, and feature engineering to ensure high-quality training datasets.
Continuously monitor model performance in production, identify areas for improvement, and iterate to enhance accuracy and efficiency.
Stay updated on advancements in AI/ML research and integrate the latest techniques into projects, including transformers, GANs, and reinforcement learning.