Senior Platform Engineer

Must-have Skills

3+ years of relevant hands-on technical experience in implementing AWS services. 
Proficiency with Python, minimum 2+ years experience, object oriented programming, and software development lifecycle, knows Basic programming constructs and have worked on python libraries (Boto3) and shell scripting.
Hands on experience with AWS services (Lambda, ECS, EKS, S3, RDS, SNS, SQS, VPC, IAM,ELB, ASG, Loading Balancing) with fundamental understanding of (Elastic Beanstalk, OpenSearch)
Experience with Airflow (Orchestration is exceptionally good to have)
Must have worked on VCS (Code commit, GitHub Bitbucket and understanding of branching strategy and version control)
Exposure to Docker containerization and Kubernetes. 
Experience with IAC - CFT, AWS CDK, Terraform 
Good understanding with EKS and related configuration to interact with AWS services (IRSA, EKS addons, etc. ) 
Must have experience with networking in case of multi-account environments by setting up of VPCs, Transit Gateway, S2S VPN Tunnel etc.
Experience with handling security recommendations from security Hub etc.
Familiarity with AWS CloudWatch for monitoring and logging AWS resources, enabling proactive monitoring of system health and performance.
Worked on Linux based systems
Experience with Agile/Scrum/DevOps software development methodologies.

Role & Responsibilities

Setting up the infrastructure and implementing all the best practices (access and networking) along with handing the key responsibilities of InfraOps developer. 
Documentation for application architecture and solutions 
Collaboration with cross-functional teams, including platform engineers, Machine learning engineers, software developers and business stakeholders, to ensure data solutions meet business needs.
Adhering to project timelines 
Communicate with non-technical stakeholders to understand their data requirements and convey the benefits of data solutions, including migration strategies

Good To Have Skills

Experience with InfraOps and DevOps practices 
Exposure of continuous integration/continuous deployment (CI/CD) pipelines for data solutions.
Implement and manage MLOps principles and best practices for Gen AI model