Machine Learning Engineer for Large Scale Model Development

Role Overview

We are seeking an experienced Machine Learning Engineer to join our team and lead the development, deployment, and operationalization of large-scale machine learning models.

About the Role

• Design and Implement MLOps Pipelines: Design, implement, and maintain scalable, reliable, and automated MLOps pipelines for model training, validation, deployment, and monitoring in cloud environments.
• Implement Model Versioning, Experimentation Tracking, and Reproducibility: Implement best practices for model versioning, experimentation tracking, and reproducibility to ensure seamless integration with our existing infrastructure.
• Develop and Operationalize Large Language Models: Develop and operationalize large language models, leveraging expertise in natural language processing and cloud-native tools.
• Collaborate with Cloud Architects and DevOps Teams: Work closely with cloud architects and DevOps teams to integrate ML models into our cloud infrastructure, ensuring seamless collaboration and minimizing downtime.
• Establish Continuous Monitoring and Logging Systems: Establish continuous monitoring, logging, and alerting systems to ensure models perform optimally in production, including retraining and updating models as needed.
• Collaborate with Data Scientists and Software Engineers: Collaborate with data scientists, software engineers, and other stakeholders to align on requirements, define best practices, and ensure seamless integration of ML solutions into our product ecosystem.
• Implement Security Best Practices: Implement and enforce security best practices in model deployment and data handling, ensuring compliance with industry regulations and maintaining a secure environment.
• Maintain Documentation and Mentor Junior Team Members: Maintain thorough documentation of MLOps processes, workflows, and tools. Mentor junior team members and contribute to the growth of the MLOps practice within the organization.
• Stay Up-to-Date with Emerging Technologies: Stay up-to-date with the latest advancements in MLOps and LLMOps technologies and trends, applying knowledge to drive innovation and improve model performance.

Requirements

• Education and Certifications: Bachelor's or master's degree in Computer Science, Computer Engineering, Data Science, AI/ML, or a related field. AI/ML, MLOps, and LLMOps certifications are highly desirable.
• Experience: A minimum of 5 years of experience in MLOps and at least 2 years of experience in LLMOps.
• Cloud and Containerization Experience: Expertise in cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• CI/CD Pipeline and DevOps Experience: Experience with CI/CD pipelines and DevOps practices, ensuring seamless collaboration between teams.
• Machine Learning Lifecycle and Model Development Experience: Deep understanding of machine learning lifecycle and model development processes, applying knowledge to drive innovation.
• Large Language Models and NLP Experience: Experience with large language models and natural language processing, leveraging expertise in NLP to drive business value.
• Distributed Computing and Big Data Experience: Knowledge of distributed computing and big data technologies, ensuring scalability and reliability.
• Data Engineering and Pipeline Development Experience: Experience with data engineering and pipeline development, ensuring seamless integration with our existing infrastructure.
• Model Monitoring Tools Experience: Familiarity with model monitoring tools like MLflow, Prometheus, and Grafana.
• Programming Skills: Strong programming skills in Python, with experience in ML libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face.
• Problem-Solving and Communication Skills: Strong problem-solving skills and the ability to work in a fast-paced, dynamic environment. Excellent communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.
• Passion for Learning: Passion for learning and staying updated with emerging technologies, driving innovation and improving model performance.