Machine Learning Engineer

Job Summary:

The client is looking for an Machine Learning Engineer:

ML Engineer would be responsible for designing, developing, implementing and supporting cutting-edge AL/ML models. To thrive in this position, the candidate must possess exceptional skills in statistics and programming, as well as a deep understanding of data science and software engineering principles.

Responsibilities:

• Collaborate and communicate effectively with team members, business stakeholders, and corporate technical resources.
• Ability to work on your own and with the team.
• Identifying and evaluating new technologies to improve performance, maintainability, and reliability of machine learning systems.
• Working closely with data scientists, software engineers, and other stakeholders to ensure that machine learning models are deployed, monitored, and updated efficiently and effectively.
• Building data pipelines, transforming data science prototypes, and applying appropriate AI/ML algorithms and tools.
• Applying software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Responsibilities will involve designing and constructing sophisticated AI/ML models, as well as refining and updating existing ones.
• Develop scalable production ready solutions that are incorporated into ML pipelines
• Establish/Follow best practices around model development and the development lifecycle
• Ensure code quality by establish baseline testing incorporated into ML Pipelines
• Evaluating and explaining the models and their outputs.
• Optimizing the model hyperparameters and handling model drift.
• Implementing version control and governance for models and data.
• Discussing various opportunities with existing and potential clients at a technical level.
• Implementing appropriate ML algorithms.
• Researching, modifying, and applying data science and data analytics prototypes.
• Searching internet for training datasets that are readily available.
• Training and retraining ML systems and models as necessary.
• Agile development with sprints lasting 2-3 weeks.
• Performs other duties as assigned.

Qualifications:

• Proven experience as a Machine Learning Engineer or similar role
• Good programming knowledge, hands-on experience with ML frameworks, libraries, agile environments, and deploying machine learning solutions using DevOps principles.
• Deep understanding of data structures, data modelling, and database management systems.
• Knowledge of the testing history throughout the CI/CD cycle.
• Ability to write robust code in Python, Java and R
• Familiarity with working on machine learning frameworks (like Keras, PyTorch or Tensorflow) and libraries (like scikit-learn)
• Understanding of the tools in the pipeline that serve different purposes, such as Continuous Integration servers, Configuration management, Deployment automation, Containers, Infrastructure Orchestration, Monitoring and Analytics, Testing and Cloud Quality tools, and network protocols.
• Knowledge of how to automate the entire DevOps pipeline, including app performance monitoring, infrastructure settings, and configurations.
• Understanding of model validation, model training, and other aspects of evaluating an ML system.
• Deep Understanding of Azure, Private Networking, MLStudio and its subcomponents, OpenAI, Github Actions and Azure DevOps.
• Understanding of typical data storage technologies and methodologies (ex: MPP and NoSQL databases, Queue based technologies, API's)
• Excellent communication skills
• Ability to work in a team
• Outstanding analytical and problem-solving skills