Must-Have:
- AI and Machine Learning Architecture: Design robust AI/ML solution architectures aligned with business and enterprise technology needs.
- Generative AI and LLMs: Apply Generative AI concepts, large language models, and prompt engineering techniques to build effective AI-driven solutions.
- Cloud Architecture: Architect and deploy scalable AI solutions on AWS and Azure cloud platforms.
- Data Architecture: Define data architecture patterns required to support AI/ML model development, integration, and operationalization.
Positive-to-Have:
- Telecom Analytics: Experience applying analytics to telecom data,
customer behavior, network insights, or business performance use cases.
- Big Data Engineering: Working knowledge of big data platforms and distributed processing frameworks for large-scale data handling.
- Spark/PySpark: Ability to build, optimize, and maintain data processing pipelines using Spark or PySpark.
- MLOps: Familiarity with model deployment, monitoring, versioning, and lifecycle management practices.
- Microservices: Understanding of microservices-based architecture and integration patterns for scalable enterprise applications.