Machine Learning Ops Engineer

Key Skills

Java Programming, Design of Experiment, Python Programming, Framework, SQL, AWS, Docker, Continuous Integration & Continuous Delivery - CI/CD, Scikit-learn, Data Analytics, Lambda, Kedro

Job Description

Together with our colleagues across the enterprise we work across all functions in Product Supply Pharmaceuticals from API production to Quality to Supply Chain and others. We solve hard analytical problems and build data science solutions with the mission to improve the overall efficiency and decision making in the company. The focus of this team lies in applications involving machine learning (ML) and operations research (OR) techniques but does not exclude other areas of advanced analytics. If you are interested in joining a dynamic team as a MLOps Engineer to ensure the data science enhanced digital products we are building are generating value in real life and are maintained also in the long run, we would like to hear from you.

As a MLOPs Engineer for Product Supply you maintain ML or OR based models in production reliably, improve the quality of production models and enhance and conduct the operations of advanced analytics models. You achive this by working closely together with Data Scientists and ML Engineers and use the flexibility of Cloud frameworks. You play the important role of assuring the long term success of ML and OR initiatives.

Your tasks:

• Operate machine learning and operations research based Applications in the Cloud, incl.
• Act timely and appropriately on notifications and alerts arising from our applications

• Lead and execute on troubleshooting, debugging and incident management for our most novel digital products with a specific focus on advanced analytics pipelines
• Lead and conduct data analysis for troubleshooting of our system to timely identify the reasons for existing challenges and report the results to peers, management and business owners adequately and transparently
• Lead and execute the handling of ad-hoc incident events, e.g. pipeline errors of our ML / OR system
• Lead and execute analyses and resolve interface issues
• Lead and conduct planned tasks such as model (re-) training and/or approval

Enhance the deployment and maintainability of machine learning and operations research models in the Cloud, incl.

• Design & implement processes which ease and automatize model deployment, e.g., CI/CD pipelines
• Propose and implement tools & frameworks which improve the maintainability of models in production
• Contribute to the establishment of an End-to End lifecycle, by taking the maintenance perspective into account
• Develop machine learning and operations research systems in the Cloud, incl.
• Lead and conduct continous improvement and enhancements activities of our ML / OR systems
• Contribute to the development activities of our data science products i.e. developing new features, working on bug fixes and/or bringing the operations perspective into projects
• Lead and handle change requests to make sure our data science products deliver the expected value to our patients and internal stakeholders the whole time
• Participate early in ML / OR product development by formulating and implementing requirements which facilitate operations

Collaborate & communicate incl.

• Collaborate with Product Leads, Data Scientists and Machine Learning Engineerings to manage the entire lifecycle of our analytics product with a focus on operations
• Present crucial narrative to peers, management, and internal customers in order to create strategic and operational changes in business

Who you are:

You have experience or are at least passionate about operating machine learning and/or operations research applications in the cloud to bring the real value of Artificial Intelligence into life. You know what it means to provide technical support for existing platforms and applications incl. debugging complex advanced analytics applications and data pipelines. You like to build up, improve and actively use cloud-based monitoring infrastructures to automatize and monitor our ML / OR systems and products.

Expert Level & previous working experience:

• Python Programming
• Frameworks to create production ready code, e.g. Kedro
• Machine Learning and/or Operations Research
• Standard Python ML Ecosystem Frameworks, e.g., scikit-learn
• Operations research - previous working experience in optimization projects leveraging mathematical solvers, e.g. Gurobi

Good knowledge & previous working experience:

• Data Engineering
• Design, implementation and maintenance of Data Pipelines
• Functional Programming
• Containarization
• Docker; experience in creating containerized tasks, e.g. on AWS ECS

Preferred:

• Experience in monitoring and operating cloud native ML systems
• Debugging of data pipelines
• AWS
• AWS Services i.e. S3, Athena, Glue, ECS, ECR, Lambda, Step Functions, Sagemaker, CloudWatch, API Gateway, IAM, DynamoDB, EMR, MWAA
• AWS Cost Management, AWS Monitoring capabilities
• Creation of above specified AWS Services via Terraform

Certifications (one or more):

• AWS Certified Developer Associate (or equivalent)
• AWS Machine Learning Specialty Certification
• AWS Certified Data Analytics
• AWS DevOps Engineer

In the exchange we will offer you:

• A flexible hybrid work model 
• Career development, 360° Feedback & Mentoring programme
• Wide access to professional development tools, trainings, & conferences
• Competitive salary, annual bonus & top performers awards
• VIP Medical Care Package (including Dental & Mental health) 
• Pension plan
• Holiday allowance (“Wczasy pod grusz?”)
• Life & Travel Insurance 
• Co-financed sport card with unlimited usage 
• Meals Subsidy in Office 
• Home Office Setup & Maintenance allowance
• New & exciting open office space coming in Spring ‘22