ML Ops is a necessity for productionizing use cases, and Senior Engineers receive salary offers of more than USD or EUR 150,000 - as did this Senior ML Ops Engineer. What is a competency profile that is highly desirable to companies?
She is an MLOps Engineer
She has become a Senior Engineer with deep expertise in backend systems, ML, and MLOps. She develops scalable, production-level systems including ad platforms, APIs, and ML tooling. Her experience includes leading cross-functional teams, deploying ML features in customer products, and optimizing infrastructure for performance and cost.
Career path
Previously, she worked as an ML engineer for two years following the completion of her M.Sc. in Data Science. Before that, she worked as a software engineer for two years. Early experience included building ML infrastructure and writing data processing pipelines for model prototyping and productionization.
She developed her expertise by deploying NLP and Computer Vision models, including contributions to model development. Her dual qualification in computer science and data science supports her focus on MLOps.
Leading deployment
You are looking at the profile of someone able to productionize models (MLOps) while advancing the company’s data infrastructure (data engineering).
You are looking at a must-have Senior MLOps profile combining all the essential elements required of an expert: The ability to productionize with a keen eye for the quality of data and code and the implementation of a rigorous test regime.
Breadth of competence
Her profile shows a meaningful connection between MLOps and data engineering. She focuses on building robust and efficient systems and is eager to solve complex engineering problems with technical precision.
The profile illustrates how her model deployments are integrated with a focus on productionization, including testing, CI/CD, and microservices.
It highlights her MLOps expertise, especially her commitment to data quality to ensure the reproducibility of models.
Her background in computer science adds another layer: Understanding the importance of writing clean code and delivering it.
Initially, she worked on both deploying language models and computer vision models, securing a breadth of experience.
Depth of expertise
Her expertise has developed further (since 2022), encompassing both technical and business aspects. Business-wise, she has optimized her platform's features, driving significant results:
Enabled over 15,000 targeted ad campaigns, generating around €40m in revenue.
Led A/B testing for ranking and automated bidding ML models, boosting long-term revenue.
Executed a database migration, saving annual costs.
On the technical front, in the ML Platform team, she:
Deployed Argo Workflows on Kubernetes to run 400-600 workflows daily.
Implemented AWS Elastic File System with Terraform & ArgoCD, enhancing storage and reducing training times.
Improved workflow performance by integrating an external Aurora RDS database, optimizing memory, and metadata handling.
Questions for the reader
If you have read this far, you may have questions or comments. Please leave them below, and I will respond.
Additionally, I have questions for you.
How do you view the importance of MLOps and deployment practices in your industry, and what are the best practices?
What type of Senior or Lead Data Professional can expect salary offers of more than USD/EUR 150,000?
Building your competency profile
Suppose you are recognized as a Senior or have 3+ years of experience in the field. In that case, you can build your competency profile to advance your career to Lead, Director, and Principal. You can choose
In-person day workshop (e.g., Berlin, or another European data metropolis) with a maximum of ten peers, and an individual follow-up to review your final draft competency profile and action plan.
Online 1-to-1 coaching (anywhere) with dedicated support until your competency profile is complete and ready for use for your promotion talk.
AI Guild
AI Guild members are experts and leaders in Data & AI, e.g., Analytics Engineering, Business Intelligence, Computer Vision, Data Analytics, Data Engineering, Data Science, Deep Learning, Machine Learning, MLOps, NLP, and Prompt Engineering.
