Artificial intelligence engineer Remote

Tal Solutions LLC
San Antonio, United States of America
3 days ago

Role details

Contract type
Permanent contract
Employment type
Part-time / full-time
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Remote
San Antonio, United States of America

Tech stack

API
Artificial Intelligence
Amazon Web Services (AWS)
Software Applications
Computer Vision
Azure
Computer Programming
Data Cleansing
Information Engineering
Distributed Computing Environment
Monitoring of Systems
Python
Machine Learning
Natural Language Processing
Recommender Systems
TensorFlow
Search Technologies
Software Engineering
Unstructured Data
Software Organization
Google Cloud Platform
Feature Engineering
PyTorch
Large Language Models
Spark
Model Validation
Generative AI
Containerization
AI Platforms
Scikit Learn
Information Technology
XGBoost
Non-relational Database
Machine Learning Operations
REST
Data Pipelines
Docker

Job description

We are seeking an experienced AI/ML Engineer to design, develop, and deploy intelligent systems that transform data into actionable insights and business value. This role focuses on building scalable machine learning solutions, predictive models, decision-support systems, and AI-powered applications that enhance operational efficiency and support data-driven decision-making. The ideal candidate combines strong software engineering fundamentals with hands-on experience developing, deploying, and maintaining AI/ML solutions in production environments.

  • Design, develop, and deploy machine learning models and AI-powered applications.

  • Build predictive analytics, classification, recommendation, forecasting, and optimization solutions.

  • Translate business requirements into scalable AI/ML systems.

  • Improve operational efficiency through intelligent automation and decision-support tools.

  • Ensure model accuracy, reliability, explainability, and performance.

  • Work with large-scale structured and unstructured datasets.

  • Develop data pipelines for model training, validation, and inference.

  • Perform feature engineering, data preprocessing, and model evaluation.

  • Collaborate with data engineering teams to ensure data quality and availability.

  • Deploy machine learning models into production environments.

  • Build and maintain scalable model-serving infrastructure.

  • Containerize and orchestrate AI services using Docker and Kubernetes.

  • Monitor model performance, drift, reliability, and system health.

  • Implement CI/CD pipelines for machine learning workflows.

  • Integrate AI services into APIs, applications, and enterprise platforms.

  • Design scalable cloud-based AI solutions using AWS, Azure, or Google Cloud.

  • Ensure security, compliance, and governance requirements are met.

  • Optimize model inference performance and infrastructure costs.

  • Partner with product managers, software engineers, data engineers, and business stakeholders.

  • Research and evaluate emerging AI/ML technologies and frameworks.

  • Drive innovation through experimentation, prototyping, and continuous improvement.

  • Provide technical guidance and mentorship to engineering teams when needed.

Requirements

  • Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.

  • 5+ years of software engineering experience.

  • 2+ years of experience building and deploying machine learning systems in production environments.

  • Strong programming skills in Python. * Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.

  • Strong understanding of statistics, machine learning algorithms, and model evaluation techniques.

  • Experience designing and consuming RESTful APIs.

  • Experience with relational and non-relational databases.

  • Strong knowledge of software development best practices and system design.

  • Experience with Large Language Models (LLMs), Generative AI, and Retrieval-Augmented Generation (RAG).

  • Experience with NLP, computer vision, recommendation systems, or predictive analytics.

  • Hands-on experience with MLOps tools and frameworks.

  • Experience with vector databases and semantic search systems.

  • Familiarity with model monitoring, explainability, and governance frameworks.

  • Experience working in regulated industries such as healthcare, finance, insurance, or automotive.

  • Knowledge of distributed data processing frameworks such as Apache Spark.

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