AI Engineer

Mexico·Guadalajaramid

Machine Learning EngineerData

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Quick Summary

Overview

Important IT company At the Latin American level, growth requires: AI Engineer Seeking a passionate, hands on technical AI /ML engineer to join our growing team and solve exciting engineering problems in semiconductor space.

Key Responsibilities

Design and Develop ML Models for high impact engineering solutions. Build, train, and optimize machine learning and deep learning models to solve complex problems using natural language processing, computer vision, and predictive analytics.

Technical Tools

azureflaskkuberneteslangchainpythonpytorchdeep-learningmachine-learning

Important IT company At the Latin American level, growth requires:

Seeking a passionate, hands on technical AI /ML engineer to join our growing team and solve exciting engineering problems in semiconductor space. The nature of problems are wide ranging giving an opportunity to grow continuously. This position will work closely with multidisciplinary team, product managers, Data engineers, data scientists, and business stakeholders to bring AI solutions to production.

Responsibilities

~1 min read

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Design and Develop ML Models for high impact engineering solutions.
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Build, train, and optimize machine learning and deep learning models to solve complex problems using natural language processing, computer vision, and predictive analytics.
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Knowledge of python, Pytorch, agentic frameworks, Langchain, RAG, capability to understand and build deep learning models on cloud or on premises compute.
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Data Collection and Preprocessing: Python preprocessing for structured and unstructured data [numeric, images, videos and documents)
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Feature Engineering: Identify, extract, and transform relevant features from raw data to improve model performance and interpretability.
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Model Evaluation and monitoring: Assess model accuracy and robustness using statistical metrics and validation techniques.
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Deployment and Integration: Knowledge of Kubernetes, Flask, Ray Serve, Azure Devops, ONNX, or cloud-based solutions. .
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Research and Innovation: Stay abreast of the latest developments in AI/ML research and technologies. Experiment with new algorithms, tools, and frameworks to drive innovation and maintain a competitive edge.
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Documentation and Reporting: Create comprehensive documentation of model architecture, data sources, training processes, and evaluation metrics. Present findings and recommendations to both technical and non-technical audiences.
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Ethics and Compliance: Uphold ethical standards and ensure compliance with regulations governing data privacy, security, and responsible AI deployment."