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2 weeks ago
As an AI Integration Engineer, you will play a pivotal role in deploying and integrating cutting-edge AI models into applications and services. You will collaborate closely with AI/ML teams to ensure seamless model consumption and deployment.
Key Responsibilities:
- Deploy and Integrate AI Models: Work with AI/ML teams to deploy machine learning models into production-ready applications and services.
- Design and Implement APIs: Create robust APIs, SDKs, and microservices that enable effortless AI model consumption.
- Ensure Reliability and Scalability: Maintain high availability, scalability, and security of AI services in production environments.
- Monitor and Improve Model Performance: Implement CI/CD pipelines, observability tools, and monitoring systems to track and enhance model performance.
- Automate Workflows: Develop automated pipelines for model retraining, versioning, and rollback processes.
- Optimize Performance: Implement caching strategies, model compression, and other techniques to improve inference speed and system efficiency.
- Cloud and Hybrid Infrastructure: Collaborate with cloud platforms (AWS, GCP, Azure) and support hybrid setups (cloud + on-prem).
Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3 – 6 years of experience in software engineering, backend development, or MLOps.
- Strong proficiency in Python with experience using FastAPI, Flask, or Django frameworks.
- Experience integrating ML models with tools like TensorFlow Serving, TorchServe, ONNX, or custom model wrappers.
- Hands-on experience with Docker, Kubernetes, and container orchestration.
- Understanding of CI/CD pipelines, MLOps practices, and model lifecycle management.
- Familiarity with cloud platforms (AWS, GCP, Azure) and their AI/ML services.
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