MLOps Engineer - Python/AWS (4-8 yrs)

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.

Skills : Python, MLOps, AWS