AI Engineer: Build AI-Powered Apps & ML Pipelines
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An application made for this job — a tailored resume and cover letter that speak straight to the posting.
Benefits offered by this job
Job summary
Triunity Software is seeking a talented AI Engineer to design, develop, and deploy machine learning models and AI-powered applications. You will collaborate with cross-functional teams to integrate AI solutions into existing software products and research emerging AI/ML frameworks to keep solutions cutting-edge.
You will build and maintain data pipelines to support model training and inference, monitor model performance in production, and contribute to best practices and technical documentation.
Qualifications
- Bachelor's or Master's degree in CS/Data Science or related field.
- Hands-on experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Proficiency in Python and data manipulation libraries (Pandas, NumPy).
- Familiarity with NLP/CV or generative AI is a strong asset.
- Experience with cloud platforms (AWS/Azure/GCP) and MLOps practices.
- Strong problem-solving and communication skills.
Responsibilities
- Design, develop, and deploy ML models and AI-powered applications.
- Collaborate with cross-functional teams to integrate AI solutions into software products.
- Research and evaluate emerging AI/ML frameworks, tools, and methodologies.
- Build and maintain data pipelines to support model training and inference.
- Monitor, test, and optimize model performance in production environments.
- Contribute to technical documentation and best practices for AI development.
Skills
Education
Tools
Job description
Triunity Software is seeking a talented AI Engineer to design, develop, and deploy machine learning models and AI-powered applications. You will collaborate with cross-functional teams to integrate AI solutions into existing software products and research emerging AI/ML frameworks to keep solutions cutting-edge.
You will build and maintain data pipelines to support model training and inference, monitor model performance in production, and contribute to best practices and technical documentation.
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