Proven experience as a Machine Learning Engineer or similar role. Overall, 10 Years
Expert level experience in ML SDLC, developing and productionizing Python and Java applications
Expert level hands on experience in deploying ML applications to AWS cloud using (SageMaker, EMR, S3, VPC endpoint etc.)
Hands on experience in AWS apps such EMR, Sage Maker , Cloud Watch, S3 Data Lake etc. ((this is a must)
Strong knowledge in CI/CD pipelines and tools such as Jenkins, Spinnaker, Bitbucket, Splunk, CloudWatch, Grafana, Dynatrace, Terraform .. etc. (this is a must)
Experience in deploying applications Kubernetes and AWS platform.
Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
Familiarity with data pipelines, HADOOP, Hive, Redshift etc.
AWS certification (Developer or Architect or ML Specialty) is a huge plus.
Experience deploying and scaling distributed systems in a cloud environment (preferably AWS implementations)
Advanced knowledge of architecture and design across all systems and cloud computing environments
Strong Programming skills in Python, Bash, Groovy and software engineering principles.
Develop high quality, secure, scalable software solutions based on technical requirements specifications and design artifacts within expected time and budget.
Research, create and evaluate technical solution alternatives for the business needs.
Experienced in building REST.
Extensive experience of Internet and Web Technologies including a strong understanding of network protocols such as DNS, HTTP, and associated tools and technologies
Strong understanding of network protocols and network security
Passionate about building an innovative and team centric culture.
Knowledge of industry-wide technology trends and best practices
Ability to work in large, collaborative teams to achieve organizational goals.