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AI Principal Engineer, VP
NatWest GroupPrincipal Engineer responsible for engineering software with a customer-centric focus. Collaborating with teams to enhance banking platforms through innovative solutions and high-quality code delivery.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in software engineering and design, with a strong focus on customer-centric solutions and the application of DevOps and Agile methodologies. Proficient in machine learning practices, including model evaluation and deployment, while ensuring engineering excellence through established standards.
Highest-signal resume keywords
Software EngineeringDevOps FrameworkMachine Learning OptimizationPython ProficiencyML Ops Standards
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software DesignDatabase ArchitectureSupervised LearningUnsupervised LearningTime-Series ForecastingFeature EngineeringModel EvaluationModel RetrainingPerformance TuningExperiment Tracking
Tools & Technologies
CI/CDCode RepositoriesBug Tracking ToolsWikis
Industry Keywords
Engineering GovernanceCustomer-Centric ApproachHigh-Quality SoftwareBank PlatformsApplications and Services
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Designing and engineering software with a customer-centric approach
- Defining, creating and providing oversight and governance of engineering and design solutions
- Working with various feature teams to produce high-quality software rapidly
- Designing and engineering complex software, scripts and tools to enable the delivery of bank platforms, applications and services
Requirements
What you’ll need- Significant experience in software engineering, software or database design and architecture
- Experience of developing software within a DevOps and Agile framework
- Strong background in building and optimizing pipelines for LLMs and traditional ML models
- Strong experience in supervised and unsupervised learning, time-series forecasting, optimization, feature engineering, model evaluation, and retraining strategies
- Strong proficiency with Python
- Hands on engineering excellence in defining and enforcing standards for ML Ops, CI/CD, experiment tracking, model deployment, observability, security, and performance tuning
- A background of working with code repositories, bug tracking tools and wikis
Benefits
Comp & perks- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development