
AI Model Architectural Specialist
2 days ago
We are seeking a highly skilled and detail-oriented AI Integration Engineer to drive the deployment and integration of AI/ML models into production environments. In this role, you will be responsible for ensuring their scalability, reliability, and security.
You will collaborate closely with data scientists, software engineers, DevOps, and product teams to streamline AI adoption and maximize impact from our AI-driven solutions.
Main Responsibilities:
- Deploy and Integrate AI Models: Collaborate with AI/ML teams to deploy machine learning models into applications and services.
- Build APIs and Microservices: Design and implement APIs, SDKs, and microservices that enable seamless AI model consumption.
- Ensure Reliability: Maintain high availability, scalability, and security of AI services in production.
- Monitor & Maintain Models: Implement CI/CD pipelines, observability tools, and monitoring systems to track and improve model performance.
- Automate Workflows: Build automated pipelines for model retraining, versioning, and rollback.
- Optimize Performance: Implement caching strategies, model compression, and other techniques to improve inference speed and system efficiency.
- Cloud & Hybrid Infrastructure: Work with AWS, GCP, or Azure environments and support hybrid setups (cloud + on-prem).
- Collaborate Cross-Functionally: Partner with DevOps, software engineers, and product teams to integrate AI capabilities smoothly into products and services.
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.
- 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.
- Excellent English communication skills – ability to work with cross-functional teams and present technical solutions clearly.
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