ML Ops

Job Overview:

We are looking for an experienced MLOps Engineer with 5+ years of expertise to join our team on a full-time basis. The successful candidate will work with a cutting-edge stack to manage and optimize machine learning pipelines, ensuring efficient deployment, monitoring, and scalability. This role will be entirely remote, and a high proficiency in English (C1) is required.

Key Responsibilities:

• ML Pipeline Management: Design, build, and maintain machine learning pipelines using Azure, Azure ML, and other cloud technologies.
• Integration and Automation: Implement and optimize workflows using tools like Kedro, Promptflow, and Airflow for seamless integration and automation.
• Model Deployment & Monitoring: Ensure efficient deployment of machine learning models with tools like FastAPI and monitor model performance using Elasticsearch.
• Collaboration: Work closely with data scientists and engineers to streamline the development-to-production process, integrating DSPy and Python-based solutions.
• Performance Optimization: Continuously monitor, troubleshoot, and improve ML models' performance, scaling the architecture as needed.

Tech Stack:

• Cloud: Azure, Azure ML
• Workflow Orchestration: Kedro, Promptflow, Airflow
• Programming: Python, DSPy
• APIs & Frameworks: FastAPI
• Monitoring & Search: Elasticsearch

Qualifications:

• Experience: 5+ years of experience in MLOps and machine learning pipeline management.
• Cloud Expertise: Strong hands-on experience with Azure and Azure ML.
• Automation & Orchestration Tools: Proven experience with Kedro, Promptflow, and Airflow.
• Programming: Proficiency in Python and related tools (DSPy).
• API Integration: Experience with deploying APIs using FastAPI.
• Monitoring Tools: Strong knowledge of Elasticsearch for monitoring and troubleshooting