Middle/Senior MLOps Engineer

The CHI Software team is not standing still. We love our job and give it one hundred percent of us! Every new project is a challenge that we face successfully. The only thing that can stop us is… Wait, it’s nothing! The number of projects is growing, and with them, our team too. And now we need a Middle/Senior MLOps Engineer.

We are seeking a skilled MLOps Engineer. The ideal candidate will be responsible for developing, deploying, and maintaining machine learning models, ensuring seamless integration and scalability. You will work closely with data scientists, software engineers, and stakeholders to implement end-to-end ML pipelines and operationalize ML solutions.

Requirements:

3+ years of MLOps background (model and data versioning, monitoring, experiment tracking, model retraining automation);
Experience with cloud platforms (AWS, Azure, GCP, etc.);
Proficiency in implementing infrastructure as code (Terraform, CloudFormation);
Competency in networking (static routing, subnets, gateways);
Knowledge of Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats);
Understanding of PowerShell scripting language;
English B2.

Nice to have:

Cloud platform-specific skills (AWS SageMaker, GCP AI Platform, Azure Databricks, etc.);
DevOps experience (e.g. CI/CD Pipelines, Infrastructure as Code, containers);
Experience in Machine Learning/ Data Science (e.g., ML algorithm selection, feature engineering, model training, hyperparameter tuning, distributed model training, supervised and unsupervised learning implementation, building model pipelines, using Machine Learning tools/libraries/frameworks);
Advanced knowledge of Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats);
Advanced knowledge of SQL and Data Modeling;
Experience creating orchestration workflows with tools such as Airflow, Kubeflow;
Knowledge of software engineering best practices across the development lifecycle, including Agile methodologies, coding standards, code reviews, source management, build processes, testing and operations.

Responsibilities:

Design, develop and maintain the automation of frameworks for iterative machine learning model development, training and inference;
Provide teams with operational support and develop solutions that provide monitoring, logging and alerting capabilities;
Automate data flows and reporting pipelines;
Manage CI/CD infrastructure;
Develop and maintain build scripts to automate deployments for multiple environments.