Senior Machine Learning Engineer MLOps Engineer

Dallas, IA - USA

Monthly Salary: Not Disclosed

Posted on: 8 hours ago

Vacancies: 1 Vacancy

Job Summary

Job Overview

We are seeking an experienced Senior Machine Learning Engineer / MLOps Engineer to design develop and deploy scalable AI/ML and Generative AI solutions. The ideal candidate will have strong expertise in machine learning deep learning frameworks and production-grade ML systems along with experience building and deploying GenAI models and AI-driven applications.

This role involves collaborating with data scientists engineers and product teams to develop high-performance ML solutions and bring models into production environments.

Key Responsibilities

Design develop and deploy scalable machine learning and deep learning models.
Build and maintain MLOps pipelines for model training deployment and monitoring.
Develop AI/ML solutions using Python with frameworks such as TensorFlow and PyTorch.
Implement and optimize Generative AI models such as GANs and Variational Autoencoders (VAEs).
Work with Natural Language Processing (NLP) techniques for text generation and language models.
Develop AI solutions involving computer vision and AI-driven image generation.
Perform data preprocessing feature engineering and dataset optimization.
Design scalable ML architectures and optimize training strategies for neural networks.
Deploy ML models into production environments and ensure reliability and performance.
Stay updated with the latest Generative AI research and emerging technologies.

Required Skills & Qualifications

Strong experience in Machine Learning and Deep Learning algorithms.
Strong programming skills in Python.
Hands-on experience with TensorFlow and PyTorch frameworks.
Experience working with Generative AI models (GANs VAEs).
Strong understanding of neural network architectures training strategies and optimization techniques.
Experience in data preprocessing feature engineering and model evaluation.
Experience deploying ML models into production environments.
Experience designing scalable AI/ML architectures.

Preferred Skills

Experience with MLOps frameworks and ML lifecycle management.
Experience in NLP and text generation models.
Experience in computer vision and AI-driven image generation.
Familiarity with cloud-based ML environments (AWS Azure or GCP).

Strong analytical and problem-solving skills.

Job Overview We are seeking an experienced Senior Machine Learning Engineer / MLOps Engineer to design develop and deploy scalable AI/ML and Generative AI solutions. The ideal candidate will have strong expertise in machine learning deep learning frameworks and production-grade ML systems along with...

Job Overview

This role involves collaborating with data scientists engineers and product teams to develop high-performance ML solutions and bring models into production environments.

Key Responsibilities

Design develop and deploy scalable machine learning and deep learning models.
Build and maintain MLOps pipelines for model training deployment and monitoring.
Develop AI/ML solutions using Python with frameworks such as TensorFlow and PyTorch.
Implement and optimize Generative AI models such as GANs and Variational Autoencoders (VAEs).
Work with Natural Language Processing (NLP) techniques for text generation and language models.
Develop AI solutions involving computer vision and AI-driven image generation.
Perform data preprocessing feature engineering and dataset optimization.
Design scalable ML architectures and optimize training strategies for neural networks.
Deploy ML models into production environments and ensure reliability and performance.
Stay updated with the latest Generative AI research and emerging technologies.

Required Skills & Qualifications

Strong experience in Machine Learning and Deep Learning algorithms.
Strong programming skills in Python.
Hands-on experience with TensorFlow and PyTorch frameworks.
Experience working with Generative AI models (GANs VAEs).
Strong understanding of neural network architectures training strategies and optimization techniques.
Experience in data preprocessing feature engineering and model evaluation.
Experience deploying ML models into production environments.
Experience designing scalable AI/ML architectures.