Lynx Analytics
Data/MachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Data / Machine Learning Engineer at Lynx Analytics. Skills: Data engineering, Machine learning, MLOps. Design data pipelines. Build data pipelines”
Industry & Context.
Problem solving
What They're Looking For.
Must Have
Tertiary qualification in computer science, engineering, mathematics, or related field, 5+ years data engineering experience, 1+ year machine learning engineering experience, Programming skills across SQL, Python, Programming skills in Java or Scala, Solid understanding of Linux, Hands-on experience with cloud platforms (AWS, GCP, or Azure), Experience building and managing data pipelines, Experience with workflow orchestration systems, Familiarity with engineering best practices, Experience developing and deploying predictive models in production, Knowledge of MLOps, Knowledge of model integration into large-scale applications
Nice to Have
PhD preferred, Specific ML framework experience, Cloud platform certs
What You'll Do.
Design data pipelines
Operationalize ML systems
Develop data pipelines
Maintain data pipelines
Build data ingestion systems
Design data analysis pipelines
Implement data analysis pipelines
Integrate analytical results
Establish automated processes
Monitor core infrastructure
Plan infrastructure enhancements
Optimize model performance
Test model performance
Track model performance
Participate in technical discussions
Participate in architectural reviews
Participate in code reviews
Adhere to data protection policies
How You'll Work.
Team & Collaboration
Client stakeholders; Data scientists; Data engineers; Client IT teams; Business owners; Client-facing teams
Communication Scope
Technical discussions
Full Job Description
ABOUT THE ROLE We are looking for a Data / Machine Learning Engineer to join our growing engineering team. In this role, you will design, build, and operationalize data pipelines and ML systems that power our analytics and AI solutions. You should have a strong foundation in data engineering and hands-on experience taking machine learning models from experimentation to production - working closely with data scientists, engineers, and client-facing teams to deliver scalable, reliable, and impactful solutions. What This Involves: Developing a deep understanding of the business problems our analytical solutions are designed to solve. Consulting continuously with client stakeholders, including IT teams, data owners, and business owners, to discover and map relevant existing data sources. Collaborating with client IT teams to define the technical architecture for analytical solutions prior to deployment. Building and maintaining data ingestion systems responsible for consolidating all necessary data sources into a centralised environment for analysis. Designing and implementing end-to-end data analysis pipelines that transform raw data into business-ready outputs. Integrating analytical results into business UIs developed in-house or into pre-existing client software systems. Establishing scalable, efficient, and automated processes for data analysis, model development, validation, and deployment. Working closely with data scientists and engineers to develop and ship new features in an iterative, continual-release environment. Monitoring core infrastructure and proactively planning enhancements to support growing analytical demands. Optimising model performance through rigorous testing, tracking, and iterative code refactoring. Participate in technical discussions, architectural and code reviews, and continuous improvement initiatives. Adhere to internal and client-mandated data protection and compliance policies, ensuring all handling, storage, and sharing of data meets
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