We are seeking a talented MLOps Engineer to join our innovative team. In this pivotal role, you'll be at the heart of our operations, bridging development, machine learning, and large language models (LLMs) to ensure seamless, efficient, and scalable deployment of our AI-driven solutions. If you're passionate about cutting-edge technology, excel in a dynamic environment, and are keen to make a significant impact, we're eager to connect with you!
Key Responsibilities
Collaboration: Work closely with ML engineers, data scientists, and software developers to integrate ML/LLM models into production.
Automation and Process Definition: Define work processes and implement automation pipelines to streamline ML/LLM development and deployment, enhancing productivity and efficiency.
Deployment and Scaling: Deploy, monitor, and scale machine learning models and large language model applications across various environments.
Monitoring and Maintenance: Implement monitoring solutions for performance and health of ML/LLM models, ensuring high availability and reliability.
Optimization: Continuously optimize ML pipelines for improved performance, scalability, and cost-efficiency. Security and Compliance: Ensure adherence to data privacy, security protocols, and compliance standards in ML/LLM operations.
Innovation: Keep abreast of the latest developments in DevOps, ML, and LLM technologies to drive continuous improvement and innovation.
Requirements: Experience: Solid experience in an ML Ops role, with a strong understanding of machine learning model deployment and lifecycle management.
Education: BS/MS in Computer Science, Engineering, or a related field, or equivalent experience.
ML/LLM Skills: Experience with machine learning lifecycle management and LLM deployment strategies.
Automation Skills: Experience with automation and orchestration tools, with a focus on enhancing the efficiency of ML workflows.
Scripting: Strong scripting skills in languages such as Python, with a focus on automating ML operations tasks.
Cloud Platforms: Familiarity with cloud services (AWS, Azure, GCP) and managing ML applications in cloud environments.
Problem-Solving: Exceptional problem-solving abilities and a keen eye for detail.
Communication: Excellent communication skills with the ability to collaborate effectively in team environments.
Nice to Have:
DevOps Background: Experience in DevOps, with familiarity in CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code, which can be leveraged to enhance ML operations.
Monitoring Tools: Experience with monitoring and logging tools (Prometheus, Grafana, ELK stack).
Large Language Models: Understanding of LLMs and their operational challenges.
Security: Knowledge of security best practices in a DevOps and ML/LLM context.
Innovative Thinking: Eagerness to explore and implement new technologies and methodologies in the realm of ML/LLM Ops.
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