The MLOps Engineer is responsible for managing the end-to-end lifecycle of machine learning models, from development through deployment, monitoring, and ongoing maintenance. This role ensures that machine learning models are deployed seamlessly and maintained efficiently, integrating automation and performance optimization throughout the process. Key areas of focus include CI/CD pipelines, model monitoring, and infrastructure automation, supporting robust and scalable ML operations.
Your role
- Manage the full lifecycle of ML models, ensuring efficient deployment, monitoring, and regular updates.
- Collaborate with data scientists and software engineers to streamline model development and production processes.
- Automate CI/CD pipelines to enable rapid and reliable model deployment.
- Monitor model performance, ensuring models meet desired performance metrics and are retrained or adjusted as necessary.
- Optimize infrastructure and workflows to support scalable, high-performance ML operations.
Offer
- Long-term freelance contract
- Solid market rates depending on seniority
- Access to top-notch projects
Requirements
- ML Project Experience: Demonstrated experience working on machine learning projects in real-world environments.
- ML Technologies & Frameworks: Proficiency in ML tools and frameworks such as Apache Airflow, scikit-learn (sklearn), MLFlow, TensorFlow, or similar.
- MLOps Expertise: Strong understanding of MLOps architecture, including best practices for continuous integration, model deployment, and monitoring.
- Data Handling: Experience with data manipulation and transformation, using tools like SQL.
- Cloud Experience: Hands-on experience working in cloud platforms like Google Cloud Platform (GCP) or similar environments.
- Programming Skills: Proficiency in Python for automating ML workflows and model development.
- Monitoring & Observability: Familiarity with monitoring tools like the ELK stack (Elasticsearch, Logstash, Kibana) for tracking model performance and infrastructure health.
- Software Engineering Practices: Solid knowledge of version control, testing, documentation, and code review processes.
- Automation & Deployment Tools: Experience with containerization and orchestration tools such as Docker, Kubernetes, OpenShift, and CI/CD tools for deployment and automation.
Nice-to-Have Skills:
- Distributed Systems: Experience with distributed data systems for batch and streaming data, using tools like S3, Spark, Kafka, or Flink.
- Advanced Analytics & NLP: Familiarity with advanced analytics techniques, data science, and natural language processing (NLP).
- Data Pipeline Construction: Experience in building and managing complex ETL pipelines for data ingestion and processing.
- System Design & Architecture: Knowledge of system design principles, especially for building scalable and secure ML platforms.
- Linux & Bash Scripting: Experience with Linux system administration and Bash scripting for automation tasks.
- Additional Programming Skills: Familiarity with statically typed languages like Scala or Java.
- Large-Scale Application Development: Experience building large-scale, secure, distributed applications.
- Agile Methodology: Comfortable working within Agile or Scrum environments.
- Open Source Contributions: Active contributions to open-source projects are a strong plus.