AI Engineer: Build & Deploy Scalable ML Solutions

VALCE T@lent Solutions; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

VALCE T@lent Solutions is seeking a skilled ML engineer to design and develop ML and deep learning models for engineering solutions in the United States. You will preprocess structured and unstructured data, perform feature engineering, model evaluation, and monitoring, and deploy models using Kubernetes, Flask, Ray Serve, ONNX, or cloud-based tools, while documenting architecture and data sources.

Collaboration with product managers and data teams is essential, with a focus on ethical AI and

Qualifications

• Strong Python and machine learning background.
• Experience with PyTorch and deep learning workflows.
• Familiarity with NLP, computer vision, predictive analytics, LangChain, and RAG.
• Knowledge of model deployment and integration tools.
• Ability to communicate technical findings clearly.
• Advanced English

Responsibilities

• Design and develop ML and deep learning models for engineering solutions.
• Preprocess structured and unstructured data, including numeric data, images, videos, and documents.
• Perform feature engineering, model evaluation, and monitoring.
• Deploy models using Kubernetes, Flask, Ray Serve, ONNX, or cloud-based tools.
• Document model architecture, data sources, training, and evaluation results.
• Collaborate with product managers, data engineers, data scientists, and business stakeholders.
• Follow ethical AI, privacy, and security practices.

Skills

Python

Machine Learning

PyTorch

NLP

Computer Vision

LangChain

RAG

Deployment Tools

Tools

Kubernetes

Flask

Ray Serve

ONNX

Job description

VALCE T@lent Solutions is seeking a skilled ML engineer to design and develop ML and deep learning models for engineering solutions in the United States. You will preprocess structured and unstructured data, perform feature engineering, model evaluation, and monitoring, and deploy models using Kubernetes, Flask, Ray Serve, ONNX, or cloud-based tools, while documenting architecture and data sources.

Collaboration with product managers and data teams is essential, with a focus on ethical AI and