Python & Generative AI Engineer

Rose International

Tampa (FL)

On-site

USD 120,000 - 180,000

Full time

4 hours ago

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Job summary

Rose International is seeking a highly skilled Python/Generative AI Developer to design, develop, and deploy advanced AI models, agents, and AI-powered apps. You will build backend infrastructure and AI capabilities from the ground up, using Python and DL frameworks while leveraging React for dynamic chat interfaces with real-time streaming and secure file uploads.

The role covers research through production deployment, including model optimization, data pipelines, and scalable AI

Qualifications

Bachelor’s or Master’s degree in Computer Science, AI, ML, or related quantitative field.
3-5 years of professional software development experience with Python backend skills.
Hands-on with Google ADK and AI/ML platforms/SDKs; AI model development.
Experience building AI agents and backend infrastructure for AI apps.
Proficiency with deep learning frameworks: TensorFlow, PyTorch, or JAX.
Strong React experience for dynamic chat UIs and streaming features.
Experience with HTTP streaming, async processing, secure uploads, advanced chat formatting.
Experience with cloud platforms (AWS/Azure/GCP) and MLOps practices.
Familiarity with Docker, Kubernetes, and AI infrastructure (MCP, REST, RAG, semantic queries).
Strong problem-solving, ownership, and communication skills.

Responsibilities

Design, develop, and deploy Generative AI models and AI agents using Python.
Build and maintain backend infrastructure enabling AI capabilities.
Integrate AI features into existing systems and new apps.
Create React chat interfaces with real-time streaming and secure file handling.
Stay current with Generative AI, NLP, and ML advancements.
Optimize models for performance, scalability, and efficiency.
Develop robust data pipelines for training, evaluation, and deployment.
Maintain AI infrastructure including MCP servers, REST endpoints, and databases.
Collaborate with data scientists and product managers to define requirements.
Ensure responsible, ethical AI deployment and maintainable codebase.
Contribute to code reviews, architecture, and technical guidance.
Troubleshoot AI models, identify bottlenecks, and resolve issues.