ML Ops Engineer

We are looking for a talented MLOps Engineer to join our growing team and help operationalize machine learning models that enhance our services and contribute to our mission.

The responsibilities

Tasks to do:

Design, build, and maintain scalable ML model deployment pipelines for production environments.
Develop and manage CI/CD processes tailored for machine learning workflows, ensuring reliable model integration and deployment.
Implement model monitoring and alerting systems to track model performance metrics (e.g., accuracy, latency) and detect data drift or model decay.
Collaborate with data scientists to streamline model retraining, versioning, and model lifecycle management.
Configure and optimize infrastructure for efficient compute resource usage (e.g., GPUs, TPUs), enabling high-performance model training and inference.
Establish best practices for data and model versioning, experiment tracking, and reproducibility.
Automate and manage ETL workflows, enabling real-time data availability for training and inference.
Ensure compliance with data protection regulations (e.g., GDPR) and enforce secure data handling practices.
Conduct A/B testing and canary releases to assess model performance in production environments.
Collaborate cross-functionally with software engineers, IT teams, and data scientists to support seamless integration of ML models.

OUR IMPOSSIBLE MATCH

Ideal Profile:

Deep knowledge of traditional ML concepts (e.g., LSTMs, RNNs, GMMs, SVMs, trees, boosting) as well as more recent deep learning fundamentals and NLP-related experience with word embeddings
Proficiency in JVM languages
Familiarity with CI/CD tools and methodologies
Proficiency with containerization (e.g., Docker) and orchestration tools
Experience with cloud-based ML platforms (e.g., Amazon Sagemaker)
Experience with common JVM search, linguistics, and other language frameworks (e.g., Lucene, StanfordNLP, OpenNLP, SparkNLP, ANTLR)
Experience using a Deep Learning Framework (e.g., Tensorflow, PyTorch, Keras)
Mature theoretical grasp of different neural networks on large-scale datasets
Deep and Fundamental understanding in signal processing concepts
A positive, collaborative, can-do attitude and a strong sense of ownership.
Familiarity with clinical data, concepts and language
Experience in model training automation with a combination of Supervised, Unsupervised, and Reinforcement methods

WHY WE ARE AMAZING

Things to expect:

A salary that meets your expectations
Annual Bonus
Health Insurance
No closed doors, remote first and flat hierarchy
Top-notch equipment
A cool and fun environment to work in, with a nice team culture and some nice dinners together :)

ML Ops Engineer

Remote, Full-Time

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