Key Responsibilities:

Cloud-Based Development: Design, develop, and deploy scalable solutions using AWS services such as S3, Kinesis, Lambda, Redshift, DynamoDB, Glue, and SageMaker. Data Processing & Pipelines: Implement efficient data pipelines and optimize data processing using pandas, Spark, and PySpark. Machine Learning Operations (MLOps): Work with model training, model registry, model deployment, and monitoring using AWS SageMaker and related services. Infrastructure-as-Code (IaC): Develop and manage AWS infrastructure using AWS CDK and CloudFormation to enable automated deployments. CI/CD Automation: Set up and maintain CI/CD pipelines using GitHub, AWS CodePipeline, and CodeBuild for streamlined development workflows. Logging & Monitoring: Implement robust monitoring and logging solutions using Splunk, DataDog, and AWS CloudWatch to ensure system performance and reliability. Code Optimization & Best Practices: Write high-quality, scalable, and maintainable Python code while adhering to software engineering best practices. Collaboration & Mentorship: Work closely with cross-functional teams, providing technical guidance and mentorship to junior developers.

Qualifications & Requirements:

7+ years of experience in software development with a strong focus on Python. Expertise in AWS services, including S3, Kinesis, Lambda, Redshift, DynamoDB, Glue, and SageMaker. Proficiency in Infrastructure-as-Code (IaC) tools like AWS CDK and CloudFormation. Experience with data processing frameworks such as pandas, Spark, and PySpark. Understanding of machine learning concepts, including model training, deployment, and monitoring. Hands-on experience with CI/CD tools such as GitHub, CodePipeline, and CodeBuild. Proficiency in monitoring and logging tools like Splunk and DataDog. Strong problem-solving skills, analytical thinking, and the ability to work in a fast-paced, collaborative environment.

Preferred Skills & Certifications:

AWS Certifications (e.g., AWS Certified Solutions Architect, AWS Certified DevOps Engineer, AWS Certified Machine Learning). Experience with containerization (Docker, Kubernetes) and serverless architectures. Familiarity with big data technologies such as Apache Kafka, Hadoop, or AWS EMR. Strong understanding of distributed computing and scalable architectures.