Job Description
About us:
Talent has no borders. Proxify's mission is to connect top developers around the world with the opportunities they deserve. So it doesn't matter where you are; we are here to help you fast-track your independent career in the right direction. π
Since our launch Proxify's developers have successfully worked with 1200+ happy clients to build their products and growth features. 3500+ talented developers trust Proxify and its network to fulfill their dreams and objectives.
Proxify is shaped by a global network of supportive talented developers interested in remote full-time jobs. Our Glassdoor (4.5/5) and Trustpilot (4.8/5) ratings reflect the trust developers place in us and our commitment to our members' success.
The Role:
We are looking for a Senior MLOps engineer for one of our clients. You are a perfect candidate if you are growth-oriented you love what you do and you enjoy working on new ideas to develop exciting products and growth features.
What weβre looking for:
-
Minimum of 5 years of professional experience in MLOps or a related field.
-
Proven experience deploying and managing machine learning models in production environments.
-
Proficiency in scripting languages (e.g. Python) and relevant MLOps tools (e.g. TensorFlow Extended Kubeflow MLflow).
-
Experience with containerization technologies (Docker) and orchestration tools (Kubernetes).
-
Strong knowledge of cloud platforms (AWS GCP or Azure) and their machine learning services.
-
Demonstrated experience implementing automated testing validation and deployment processes for machine learning models.
Must-have skills:
-
Python
-
Azure / AWS / GCP
-
Grafana / Prometheus
-
SQL
Responsibilities:
-
Develop and implement a comprehensive MLOps strategy ensuring the seamless integration of machine learning models into our production environment.
-
Design build and maintain end-to-end machine learning pipelines encompassing data preprocessing model training deployment and monitoring.
-
Collaborate with cross-functional teams to design deploy and manage scalable infrastructure for machine learning workloads. Utilise containerization technologies (e.g. Docker Kubernetes) and cloud platforms (e.g. AWS GCP or Azure).
-
Implement and manage CI/CD pipelines for machine learning models enabling automated testing validation and deployment.
-
Establish robust monitoring and logging systems to track the performance of machine learning models in production ensuring timely detection of anomalies and potential issues.
-
Work closely with data scientists software engineers and other stakeholders to understand model requirements deployment needs and data dependencies.
-
Implement security best practices for machine learning systems and ensure compliance with relevant regulations and standards.
What Proxify offers
-
Career-accelerating positions at cutting-edge companies
Discover exclusive long-term remote engagements at the world's most interesting product companies.
-
Hand-picked opportunities just for you
Skip the typical recruitment roadblocks and biases with personally matched engagements.
-
Fast-track your independent developer career
Start small and gain more freedom to take on new engagements as you build your independent developer career.
-
A recruitment process that values your time
Only one hiring process with the possibility of several positions without any additional tests.
Explore More
Date Posted
02/19/2024
Views
0