Principal Architect

Role & responsibilities

1- Architectural Leadership

• Design, implement, and optimize large-scale AWS ML infrastructure and software systems.
• Ensure performance, scalability, security, and resilience of the overall architecture.

2- Machine Learning Infrastructure

• Build systems using AWS services, including Lex for conversational interfaces, S3 for storage, SQS for queuing, and other AWS tools.

3- Cross-Functional Collaboration-

• Partner with Data Scientists, ML Engineers, Software Developers, DevOps, and Product Managers to deliver technical solutions that align with business objectives.

4. Cloud Architecture Best Practices

• Define and implement cloud infrastructure best practices focused on performance optimization, fault tolerance, cost management, and scalability for ML pipelines and agent-based systems.

5. Solution Design

• Develop high-level architecture that integrates various AWS services, databases, and agent-based and traditional software systems.

6. Mentorship

• Mentor technical team members, fostering knowledge sharing and professional growth.

7. Strategic Input

• Contribute to the long-term technical vision and define roadmaps for technology and ML initiatives.

8. Continuous Improvement

• Stay updated with emerging technologies and AWS advancements in ML and cloud architecture to maintain a competitive edge.

Preferred candidate profile

1. Experience:

• 7+ years of experience in designing, architecting, and deploying ML infrastructure on AWS.
• Hands-on expertise with AWS services such as Lex, S3, SQS, and other AWS-based data processing tools.

2. AWS Proficiency:

• In-depth knowledge of AWS services, particularly for ML workflows, data lakes, and real-time systems.

3. Architectural Expertise:

• Proven track record of creating scalable and reliable architectures for ML and agent-based software systems.

4. ML Systems Understanding:

• Experience with ML frameworks and integration with AWS ML services such as SageMaker.

5. Cloud Architecture:

• Expertise in cloud design patterns, including microservices, serverless computing, containerization (ECS, EKS), and event-driven architectures.

6. Databases and Data Pipelines:

• Proficiency in AWS database systems (RDS, DynamoDB) and building efficient ETL processes and data lake architectures.

7. Leadership and Mentorship:

• Demonstrated ability to lead teams and provide technical direction for complex projects.

8. Communication:

• Excellent written and verbal communication skills to articulate complex technical concepts to stakeholders across all levels.

Preferred Skills (Optional):

• AWS Certifications (e.g., AWS Certified Solutions Architect Professional).
• Familiarity with agent-based software systems and their applications in data-driven environments.
• Hands-on experience with DevOps practices, including CI/CD pipelines and infrastructure-as-code tools like CloudFormation or Terraform.