AWS engineer

This role is for one of the Weekday's clients

Min Experience: 2 years

Location: India

JobType: full-time

We are seeking a passionate and results-driven AWS Engineer with a strong foundation in Artificial Intelligence (AI) and cloud infrastructure to join our growing team. As part of our AI initiatives, you will be responsible for designing, developing, deploying, and maintaining scalable cloud-based solutions on AWS. You’ll work closely with data scientists, ML engineers, and software developers to bring AI models into production and optimize the infrastructure supporting intelligent applications.

Requirements

Key Responsibilities:

• Design and implement robust, secure, and scalable cloud solutions using AWS for AI/ML workloads.
• Deploy and manage AI/ML models using services such as Amazon SageMaker, Lambda, ECS/EKS, and EC2.
• Collaborate with AI/ML teams to understand model requirements and translate them into cloud-native architectures.
• Automate cloud infrastructure provisioning and deployment using Infrastructure as Code (IaC) tools such as CloudFormation or Terraform.
• Monitor, troubleshoot, and optimize deployed models and pipelines to ensure high performance and reliability.
• Implement CI/CD pipelines tailored for ML workflows, ensuring smooth model training, testing, and deployment cycles.
• Ensure data security and compliance with best practices in managing training datasets and inference endpoints.
• Continuously research and implement the latest AWS tools and best practices related to AI and cloud engineering.

Key Skills and Qualifications:

• 2+ years of hands-on experience working with AWS cloud services, particularly in AI/ML contexts.
• Proficiency in AWS services such as S3, EC2, Lambda, SageMaker, IAM, CloudWatch, ECR, ECS, EKS, and CloudFormation.
• Familiarity with deploying and maintaining machine learning models in production environments.
• Understanding of core AI/ML concepts, frameworks (such as TensorFlow, PyTorch, or Scikit-learn), and their deployment lifecycle.
• Strong programming/scripting skills in Python, Bash, or Node.js.
• Experience with Docker and container orchestration (preferably Kubernetes).
• Exposure to monitoring, logging, and alerting tools like CloudWatch, Prometheus, or Grafana.
• Knowledge of CI/CD practices and tools (e.g., GitHub Actions, Jenkins, CodePipeline).
• Bachelor’s degree in Computer Science, Engineering, or a related technical field.

Preferred Qualifications:

• AWS Certification (e.g., AWS Certified Machine Learning – Specialty, Solutions Architect Associate/Professional).
• Experience with MLOps frameworks and model versioning tools like MLflow or Kubeflow.
• Familiarity with data preprocessing pipelines and big data tools like Apache Spark, Glue, or Redshift.
• Experience working in an Agile/Scrum environment.