Our client is an Information Technology and Business Consulting Company that provides highly specialised solutions to large and small enterprises in both the private and public sectors. Their team of experts leverages cutting-edge technologies to address complex business challengesand drive digital transformation.
They are seeking a highly experienced AWS MLOps Engineer with a minimum of 3 years of AWS experience and at least 5 years in Machine Learning. The ideal candidate will play a crucial role in designing, implementing, and maintaining machine learning (ML) solutions on the AWS platform. As an AWS MLOps Engineer, you will work closely with cross-functional teams to deploy and operationalize ML models, ensuring seamless integration into their clients' environments.
Responsibilities differ across client engagements but may include: • Architect and Deploy ML Solutions: Design and implement end-to-end ML pipelines on the AWS platform, ensuring scalability, reliability, and compliance with banking sector regulations. • Model Training and Evaluation: Collaborate with data scientists to facilitate the training and evaluation of ML models, optimizing for accuracy and efficiency. • Infrastructure Management: Build and manage infrastructure on AWS, including EC2 instances, S3 buckets, and other relevant services, adhering to the highest security standards required by the banking sector Automation and Orchestration: Implement automation and orchestration tools to streamline ML workflows and enhance operational efficiency. • Monitoring and Logging: Develop robust monitoring and logging mechanisms to track the performance of ML models in production, identifying and addressing issues proactively. • Security and Compliance: Implement security best practices and ensure compliance with data protection regulations in ML workflows and infrastructure. • Collaboration and Documentation: Work closely with cross-functional teams, providing technical expertise and documentation to support knowledge transfer.
Competencies: AWS Expertise: In-depth knowledge of AWS services, with a focus on those relevant to ML, such as SageMaker, Lambda, Glue, and others. • MLOps Proficiency: Hands-on experience with MLOps practices, including model versioning, continuous integration, and continuous deployment (CI/CD) for ML. • Programming Skills: Proficient in programming languages such as Python or C#, with the ability to write clean and efficient code for ML workflows. • Containerization and Orchestration: Experience with containerization tools (Docker) and orchestration frameworks (Kubernetes) for deploying and managing ML applications. • Infrastructure as Code (IaC): Familiarity with IaC tools like Terraform or AWS CloudFormation for automating infrastructure deployment. • Monitoring and Logging Tools: Knowledge of monitoring and logging tools such as CloudWatch, ELK stack, or Prometheus for tracking ML model performance. • Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams and clients