Description
At Calabrio, we are revolutionizing the way organizations connect with their customers, and we empower businesses to elevate every interaction to new heights. Our cutting-edge cloud platform Calabrio ONE, coupled with AI-driven analytics tools, unlocks the true essence of customer sentiment, turning data into actionable insights at lightning speed.
Calabrio is looking for a highly skilled and experienced AWS Machine Learning Operation Specialist to perform a key role in our digital transformation program, and deliver exceptional customer experience supported by trusted, and resilient business solutions. As an AWS MLOps Specialist, you will play a pivotal role in design, deploy, and maintain scalable cutting-edge ML and ETL pipelines. The ideal candidate should have a strong background in AWS cloud infrastructure, and a proven track record of delivering successful projects in the field.
Calabrio has embarked journey and is truly committed to establishing a value fabric that transforms its customer, employee, and stakeholder experiences through seamless integrated, agile, data-driven, and secure Digital Services. Such an endeavor requires leaders passionate about customer experience and committed to consistently delivering value while focusing on digital services with inherent trust and resilience.
We are seeking a highly skilled and motivated AWS MLOps (Machine learning operation) Specialist to join our dynamic team. This role bridges the gap between machine learning operations, ETL operations, and traditional DevOps, focusing on deploying, managing, and optimizing ML and ETL workflows and infrastructure. The ideal candidate will have hands-on experience in both MLOps and DevOps practices and expertise in managing AWS cloud infrastructure. An AWS certification or significant experience in AWS environments is highly desirable.
Key Responsibilities

PREFERRED QUALIFICATIONS:

AWS Certified Solutions Architect, Developer.
Experience with hybrid or multi-cloud setups.
Knowledge of serverless architecture and microservices.

SOFT SKILLS:

Strong problem-solving and analytical skills.
Excellent communication and collaboration skills to work with cross-functional teams.
Proactive and self-driven with a focus on continuous learning and improvement.
Benefits

1. DEVOPS RESPONSIBILITIES:

Design, implement, and manage CI/CD pipelines for software applications.
Automate infrastructure provisioning, configuration, and scaling using AWS CDK and tools like Terraform or CloudFormation.
Monitor and troubleshoot production systems to ensure high availability and reliability.
Develop robust logging, monitoring, and alerting solutions using tools like Datadog, CloudWatch, or Prometheus.

2. MLOPS RESPONSIBILITIES:

Design, deploy, and maintain scalable ML and ETL pipelines in production environments.
Implement CI/CD workflows for ML models and ETL, ensuring reliable and automated deployment processes.
Monitor and optimize ML/ETL performance, ensuring efficient resource utilization.
Collaborate with ML Engineers to integrate ML/ETL workflows into scalable production systems.

3. AWS CLOUD RESPONSIBILITIES:

Architect and manage AWS-based infrastructure for both machine learning and software systems.
Optimize AWS services (e.g., EC2, ECS, S3, Lambda, SageMaker) to meet performance and cost requirements.
Ensure security and compliance in AWS environments through best practices and tools.
Leverage AWS services for deploying containerized applications using ECS, EKS, or Fargate.
Requirements
Bachelor’s degree in Computer Science, Engineering, or a related field.
Proven experience in MLOps, DevOps, or related roles with a strong understanding of both domains.
Proficiency in Python and scripting languages like Bash.
Experience with CI/CD tools (e.g., Azure DevOps, Jenkins, GitHub Actions, GitLab CI/CD).
Strong expertise in containerization (Docker) and orchestration tools (Kubernetes).
Hands-on experience with AWS infrastructure and services, with preference for AWS-certified professionals.
Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
Knowledge of ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and model deployment practices.
Experience with SQL and NO-SQL databases like PostgreSQL, Snowflake, DynamoDB, or MongoDB.