Principal Software Engineer - AWS MLOps and Data Engineer

Technical Skills

• Amazon Sage Maker:
  In-depth knowledge of Sage Maker, including domain setup, configuration, and infrastructure management.
• Cloud Knowledge: A deep understanding of cloud computing concepts, especially related to Amazon Web Services (AWS).
• Infrastructure Design:
  Ability to design and implement MLOPs cloud solutions, considering scalability, security, and performance.
• Experience:
  
  Practical firsthand experience with cloud MLOps and Data Analtics platforms, preferably AWS Sage Maker, Glue, EMR, Athena.
• Best Practices:
  Familiarity with best practices for MLOps and Data Engineering.
• EC2 Instances:
  Understanding of EC2 instance types and their suitability for AWS Sage Maker.
• S3:
  Proficiency in using Amazon S3 for data storage and Sage Maker input/output.
• IAM:
  Ability to manage permissions and access control using Identity and Access Management.
• Lambda:
  Knowledge of serverless computing for automating tasks.
• ML & Data Pipelines:
  Experience with creating data pipelines using AWS Sage Maker services integrated with Glue and EMR.
• Monitoring and Troubleshooting:
  Proficiency in monitoring Sage Maker cluster health, identifying bottlenecks, and resolving issues.
• Cost Optimization:
  Strategies to tag Sage Maker resources with an eye on optimizing costs and observability.

• Encryption:
  Understanding of data encryption at rest and in transit to ensure secure data analytics cloud environment.
• Security Groups and VPC:
  Knowledge of network security and virtual private clouds.
• Compliance Controls:
  Ensuring compliance with industry standards and regulations.

• Langauge Proficiency:
  Python, R, Spark, SQL in scripting languages for automating tasks.
• MLOPs:
  Ability to collaborate with the business to optimize MLOps process, and model lifeycle using Sage Maker
• Infrastructure as Code (IaC):
  Ability to assist Dev Ops engineers to develop proper Terraform templates used to provision AWS analytics infrastructure.

• Snapshotting:
  Familiarity with taking EMR cluster snapshots for backup and recovery.
• High Availability:
  Implementing strategies for fault tolerance and disaster recovery.

• Communication:
  Effective communication with stakeholders, developers, and data engineers.
• Problem-Solving:
  Analytical thinking to address complex issues.
• Adaptability:
  Keeping up with evolving technologies and best practices.
• Decisiveness:
  Make informed decisions, especially when dealing with complex architectural choices.
• Business Acumen:
  Understand business requirements and align technical solutions accordingly.
• Continuous Learning: A zeal for staying updated with evolving cloud technologies.

• Experience:
  
  Senior AWS Cloud Engineers must have 3 to 5 years of firsthand experience in designing and building cloud MLOps and Data Analytics applications.
• Certifications:
  
  AWS Professional Developer or Data certifications are desired for senior roles.
• Aligns business strategy with software solutions.
• Proposes, designs & codes software solutions to address complex business needs. Oversees technical and procedural documentation required.
• Leads complex problem solving.
• Provides technical guidance and support to colleagues and solution development.
• Displays an innovative approach to apply modern principles, methodologies and tools to advance business initiatives and capabilities.

Application Design, Architecture – Knowledge of application design activities, tools and techniques; ability to utilize these to convert business requirements and logical models into a technical application design.

Packaged Application Integration – Knowledge of and the ability to implement packaged application software and integrate it with company applications, databases and technology platforms.

Product and Vendor Evaluation – Knowledge of and ability to implement processes for the evaluation and selection of products, tools, services and infrastructure components ensuring they are in line with an organization's business needs and architectural principles.

Software Process Improvement (SPI) – Knowledge of formal software process improvement disciplines, and ability to assess and…