Principal ML Analytics Engineer

This is a fully remote position, open to applicants in Ireland.

📋 Description

• Design, develop, install, deploy, test medium-scale analytics models and AI applications to support business objectives and client outcomes.

• Create and implement training pipelines for machine learning models.

• Develop advanced MLOps pipelines for the deployment and updating of machine learning models and analytics applications.

• Construct innovative feature engineering pipelines to extract and prepare data for optimizing machine learning model performance.

• Train and assess models using varying datasets and performance metrics, selecting and applying suitable machine learning algorithms.

• Sustain and enhance models to boost accuracy and ensure reliability monitoring.

• Establish effective monitoring systems to track model performance and pinpoint issues.

• Identify and address biases in machine learning models to foster fairness.

• Draft comprehensive specifications for machine learning models and processes to ensure reproducibility and maintenance.

• Deliver insights and performance tuning suggestions derived from machine learning models to guide business decisions.

• Advocate for ethical principles and responsible AI guidelines in the development of machine learning.

• Seamlessly integrate machine learning models into production environments.

• Create and deploy APIs for model access and prediction requests.

• Apply best practices and methodologies in MLOps to facilitate efficient and scalable management of the model lifecycle.

• Integrate leading AI services and machine learning models with existing applications and DevSecOps deployment pipelines.

• Adopt Agile development methodologies and implement SDLC processes in all development efforts.

• Collaborate with data science practitioners, finance and business analysts, actuaries, and engineers to meet model objectives.

• Provide effective mentorship to other engineers.

• Champion a "data-first" approach in problem-solving and the design of IT solutions.

⛳️ Requirements

• A Bachelor’s degree in a computing-related field or equivalent industry experience is mandatory.

• 7-10 years of industry experience is preferred, showcasing senior roles in software engineering and data analytics.

• Extensive knowledge of artificial intelligence, generative AI, and data science concepts, including machine learning algorithms, statistical methods, and data mining techniques.

• Demonstrated proficiency in various platforms managing the lifecycle of machine learning models, including practices and tools within MLOps such as model versioning, monitoring, and deployment.

• Comprehensive knowledge of data science platforms and big data technologies.

• Familiarity with AI and ML service platforms like Dataiku, Amazon SageMaker, or Azure Machine Learning.

• Strong programming skills in mathematical/data science languages, particularly Python, R, and SQL.

• Capability to utilize these languages for data exploration, manipulation, and analysis.

• Expertise in database management with relational and non-relational databases, as well as Cloud data warehousing platforms featuring multi-clustered distributed architectures that are highly scalable and support elastic compute.

• Robust understanding of data streaming technologies and event-driven architectures.

• Proven ability to enhance machine learning models through ongoing data monitoring, retraining, and adaptation to evolving data patterns.

• Advanced deployment skills in managing the lifecycle of AI and machine learning models, from analytics sandboxes to production environments, while effectively monitoring their performance.

• Strong capability to design, develop, and maintain AI and machine learning systems, utilizing best practices in MLOps principles.

• In-depth knowledge of bias detection techniques to ensure ethical and unbiased machine learning models.

• Comprehensive understanding of data and model governance, bias detection, and ethical AI.

• Demonstrated expertise in data governance principles, including data quality, security, and compliance.

• Proven history of designing, developing, and maintaining machine learning and AI applications, covering data preparation, model training, evaluation, and deployment.

• Strong familiarity with agile DataOps, MLOps, and AIOps methodologies, facilitating rapid development, testing, and deployment of machine learning solutions.

• Solid understanding of API integration and authentication mechanisms for machine learning models and analytics applications.

🏝️ Benefits

• Flexible work arrangements.

• Professional development opportunities.