Machine Learning Engineer

Introduction As part of a cross-functional team of engineers, data scientists, and product owners, you will be responsible for designing, implementing, optimizing, and maintaining our machine learning operations (MLOps) infrastructure. If you are passionate about bringing machine learning models from development to production seamlessly and efficiently, Avrioc is the place to be for you!

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• 3+ years of professional experience in MLOps or related fields.

• Hands-on experience with containerization technologies like Docker and container orchestration tools such as Kubernetes and EKS.

• Good to have knowledge in implementing LLMOps best practices for model performance monitoring, drift detection, prompt management, and feedback loops.

• Good to have knowledge in model optimization techniques including quantization, distillation, and pruning for efficient inference on AWS infrastructure.

• Strong background in establishing comprehensive monitoring and alerting mechanisms for model performance, latency, and resource utilization

Technical Skills:

• AWS / • Kubernetes / • Python / • PyTorch /TensorFlow / • Slurm / • Ray

• Nvidia DGX / • Kubeflow, MLflow / • Langchain, ChainLit, LLaMAIndex

Additional Skills:

• Excellent problem-solving skills and ability to work independently.

• Strong communication and collaboration skills to work effectively with cross-functional teams.

• Ability to stay updated with the latest technology trends and drive best practices in MLOps

• Containerization:

Implement and manage Docker-based containerization and orchestration using Kubernetes and EKS for deploying large language models (LLMs).

• LLMOps Practices:

Apply and implement LLMOps best practices for continuous monitoring of model performance, detecting model drift, managing prompts, and establishing feedback loops for continuous improvement.

• Model Optimization:

Utilize techniques such as quantization, distillation, and pruning to optimize LLM models for efficient inference on AWS infrastructure.

• Monitoring and Observability:

Develop and maintain comprehensive monitoring and alerting systems to track LLM performance, latency, resource utilization, and identify potential biases.

• Prompt Engineering and Management:

Create strategies for prompt engineering and management to enhance LLM outputs, ensuring consistency and safety.

• Collaboration:

Collaborate closely with data scientists, researchers, and software engineers to ensure seamless integration and deployment of machine learning models.

• Model Deployment:

Ensure that machine learning models are properly versioned and deployed into production, staging, or testing environments automatically.

• CI/CD:

Design, implement, and manage CI/CD pipelines tailored for machine learning workflows to automate deployment processes.

• Operational Excellence:

Set up and fully implement scalable machine learning operations environments. Continuously monitor, optimize, debug, and automate MLOps pipelines for increased quality and efficiency at pipeline, module, and system levels.

• Documentation:

Document and track all systems, pipelines, and best practices to maintain a high standard of operations.

• Continuous Learning:

Keep abreast of the latest technology trends to drive standard methodologies and stay ahead of the curve.

Common responsibilities:

• Comply to Avrioc’s Information security and Information service management policies, procedures, and standards.

• Maintain confidentiality and integrity of information and attend mandatory Information security trainings.

• Report information security incidents through Avrioc’s established incident reporting channel.