Qualificaitons:

• Bachelor’s or equivalent experience in Computer Science, Electrical, Computer, or Mechatronics Engineering, or related field
• Proficiency in either Python, and expertise in modern data storage systems.
• Experience with data science tools (Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment.)
• Experience designing and developing Python web applications and API’s
• Experience with Flask, Django, FastAPI etc. frameworks
• 3+ year of experience supporting Data Science teams with deploying models
• Experience with CI/CD tools like Jenkins, Bamboo, Octopus, etc.
• Git/GitHub integration with CI/CD tools
• Up-to-date on the latest deep neural net research and architectures, both in understanding the theory and motivations behind the techniques, as well as how to implement them in the ML framework of your choice
• Experience with Big Data Frameworks such as Spark is a plus
• Experience with containerization techniques and orchestration
• Experience deploying services/models on Kubernetes is a plus

Job Descirption:

• Collaborate with other project team members and ensure that analytics best practices are implemented.
• Accountable on technical input into the design, development, implementation of ML solutions
• Own the configuration and Implementation of ML pipeline using the agreed technologies components
• Accountable (own) the scalability and high-availability of the ML solutions.
• Accountable (own) the completeness and accuracy of the MLOps Requirements.   
• Accountable for quality assurance and testing of ML solutions within the scope of the project.
• Meet project milestones.                                                                                                            
• Build an effective relationship with internal and external stakeholders.                                    
• Able to perform multiple deployments concurrently.                                                                  
• Take ownership of production systems with a focus on delivery, continuous integration, and automation of machine learning workloads
• Provide technical mentorship, guidance, and quality-focused code review to data scientists and ML engineers

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