AI Solutions Architect

Key Skills

1. Technical Expertise:
   
• In-depth understanding of AI/ML algorithms, frameworks (TensorFlow, PyTorch), and cloud platforms (AWS, GCP, Azure).
• Knowledge of APIs, microservices, and system integration.
2. Software Architecture:
   
• Proficiency in designing distributed systems and handling high-availability requirements.
• Expertise in tools like Kubernetes, Docker, and CI/CD pipelines.
3. Data Engineering:
   
• Familiarity with ETL processes, data lakes, and data warehouses.
• Experience with tools like Apache Spark, Kafka, or Snowflake.
4. Cloud Computing:
   
• Hands-on experience with AI services on AWS (SageMaker), Google Cloud (Vertex AI), or Azure (ML Studio).
• Understanding of serverless architectures and edge computing.
5. Problem-Solving and Analytical Thinking:
   
• Ability to analyze complex problems and design innovative AI-driven solutions.
• Strong understanding of trade-offs in architecture design (e.g., cost vs. performance).
6. Communication and Collaboration:
   
• Excellent communication skills to explain technical concepts to business stakeholders.
• Leadership abilities to coordinate cross-functional teams.
7. Regulatory and Ethical Knowledge:
   
• Awareness of AI ethics, data governance, and regulatory standards (e.g., GDPR, HIPAA).

Tools and Technologies

• AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn
• Cloud Platforms: AWS (SageMaker, Lambda), Google Cloud (Vertex AI, BigQuery), Azure (ML Studio, Cognitive Services)
• Data Engineering: Apache Kafka, Apache Spark, Snowflake
• DevOps: Docker, Kubernetes, Jenkins
• Monitoring Tools: Prometheus, Grafana, Datadog
• Programming Languages: Python, Java, Scala