This is a remote position.

About the Company - Aveni is an award-winning technology company revolutionising the financial services industry. We utilise advanced AI to deliver scalable efficiency, leveraging cutting-edge Natural Language Processing (NLP) and Large Language Model (LLM) expertise. Our deep financial services domain knowledge allows us to drive unparalleled productivity and compliance for our clients. Having secured series A funding in July, our team is expanding fast, with strong growth plans predicted over the next few years.

About the Role - We are looking for a skilled and experienced MLOps Engineer to design, implement, and optimise machine learning infrastructure. You’ll be crucial in managing the lifecycle of ML models, from deployment to monitoring and maintenance, in a collaborative, fast-paced environment.

Responsibilities

• Develop, deploy, and maintain scalable MLOps pipelines to automate key workflows.
  
• Ensure solutions are platform-independent and support multi-cloud environments.
  
• Use Infrastructure-as-Code (IaC) tools like Terraform or CloudFormation for automated deployments.
  
• Collaborate with data scientists, engineers, and other teams to create optimised, production-ready solutions.
  
• Deploy and orchestrate ML models using Docker, Kubernetes, and other tools; experience with deploying large-scale LLMs is necessary.
  
• Implement monitoring and logging for ML models, ensuring robust alert systems and dashboards for model health and performance.
  
• Optimise CI/CD pipelines for ML models to enhance speed and reliability.
  
• Develop and enforce best practices for MLOps, including versioning and scalable deployments.
  
• Support the transition from AWS to a multi-cloud environment while ensuring compatibility and reliability.
  

Requirements

• Demonstrated experience in MLOps or related fields focusing on production-level ML deployment.
  
• Hands-on experience with AWS, Azure, GCP, and platform-agnostic cloud solutions.
  
• Proficiency with Docker, Kubernetes, and IaC tools like Terraform.
  
• Experience with CI/CD pipelines using GitLab CI/CD, Jenkins, etc.
  
• Strong understanding of ML model lifecycle management.
  
• Familiarity with popular ML frameworks (e.g., TensorFlow, PyTorch).
  
• Proficient scripting skills in Python, Bash, or similar.
  

Preferred Skills

• Experience transitioning from AWS to a multi-cloud setup.
  
• Familiarity with cloud-native storage and data engineering workflows.
  
• Understanding of distributed systems and high-performance computing.

Benefits