Machine Learning Engineer
Skills
Artificial IntelligenceData MiningData ScienceLearningMlopsMachine LearningMachine Tools
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Automating CI/CD pipelines to account for data, code, and model changes
- Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge
- Querying and working with databases, testing ML models, Git and version control, frameworks like Flask, Fast API
- Proficiency in tools such as Docker and Kubernetes
- Familiarity with experiment tracking frameworks such as MLflow
- Setting up and automating data pipelines using tools such as Airflow, Kafka and Rabbitmq
- Providing best practices and executing POC for automated and efficient model operations at scale
- Good to have hands-on experience using large foundation models and associated tool chains (e.g. Lang chain) and APIs to build applications
- Automating AI/ML model deployment and setting up monitoring for the ML pipeline
Nice to have
- CI/CD automation
- Machine learning proficiency
- Database querying
- ML model testing
- Version control (Git)
- Flask/Fast API proficiency
- Docker/Kubernetes proficiency
- Experiment tracking (MLflow)
- Data pipeline automation (Airflow, Kafka, Rabbitmq)
- Experience with large foundation models
- AI/ML model deployment automation
Day to day
- Automating CI/CD pipelines for data, code, and model integration
- Programming with ML algorithms and frameworks
- Setting up and automating data pipelines with Airflow, Kafka, Rabbitmq