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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and maintaining large-scale AI systems, with a strong focus on machine learning pipelines, data quality, and model evaluation. Capable of translating complex AI challenges into actionable technical projects while collaborating effectively with research and enterprise partners.
Highest-signal resume keywords
Advanced Python Engineering SkillsExperience with LLMs and Agentic SystemsData Pipeline and ML Infrastructure DevelopmentTechnical Partnering in Ambiguous EnvironmentsStrong Understanding of Data Quality and Taxonomy Design
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningData CurationModel EvaluationWorkflow AutomationMulti-Turn WorkflowsAI AutomationData Taxonomy DesignEvaluation RubricsQuality AssuranceHands-On Engineering
Soft Skills
Technical OwnershipCollaborationProblem SolvingCommunicationIndependence
Tools & Technologies
LLM ToolingModel-Evaluation StacksML Experimentation PlatformsData Labelling SystemsEvaluation Harnesses
Industry Keywords
Applied AIAI InfrastructureResearch-Focused EngineeringStartup ExperienceEnterprise Stakeholders
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Work directly with AI research teams and enterprise partners to define research goals, technical requirements, and project direction
- Translate ambiguous AI and machine-learning problems into clearly scoped technical projects
- Act as a technical implementation partner across research, engineering, product, and stakeholder teams
- Move between research questions, technical architecture, hands-on engineering, and partner-facing execution
- Own technical systems from discovery through implementation, deployment, iteration, and ongoing reliability
- Build large-scale data-intelligence systems for collecting, organising, evaluating, and improving training and evaluation data
- Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement
- Develop infrastructure for model inference, experimentation, evaluation, and deployment
- Design reliable workflows supporting advanced AI research and production environments
- Build and maintain systems supporting complex data and machine-learning workloads
- Develop LLM applications including multi-agent systems, tool-using agents, RAG workflows, and human-in-the-loop systems
- Build evaluation harnesses and infrastructure for assessing agent and model behaviour
- Develop systems that extend AI experiments into reliable, repeatable, multi-turn workflows
- Implement agentic automation across technically complex business and research processes
- Apply modern LLM tooling and model-evaluation approaches to production-oriented AI systems
- Design data taxonomies, labelling systems, evaluation rubrics, and quality frameworks
- Improve dataset structure and quality to support stronger model performance and research outcomes
- Develop workflows for dataset curation, annotation, validation, and quality assurance
- Analyse data and model behaviour to identify opportunities for system improvement
- Apply rigorous standards to training data, evaluation datasets, and research workflows
- Project scope and research priorities may evolve depending on technical and partner requirements
Requirements
What you’ll need- Strong professional experience as a software, machine-learning, applied AI, or infrastructure engineer
- Advanced Python engineering skills with experience building and shipping production systems end to end
- Practical experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation
- Experience building or maintaining data pipelines, ML infrastructure, model-evaluation systems, or research workflows
- Strong understanding of data quality, taxonomy design, labelling workflows, and dataset curation for AI systems
- Ability to operate independently in ambiguous, partner-facing environments with strong technical and product ownership
- Comfortable working directly with researchers, technical partners, founders, and enterprise stakeholders
- Experience within a startup, AI infrastructure company, applied AI organisation, or research-focused engineering team is advantageous
- Experience building multi-turn agents, agent-evaluation systems, workflow automation, or human-in-the-loop AI is advantageous
- Familiarity with modern LLM tooling, agent frameworks, model-evaluation stacks, and ML experimentation platforms is beneficial
- Experience acting as a technical partner to external customers, research teams, or strategic enterprise accounts is strongly valued
- Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
Benefits
Comp & perks- Remote work
- Travel required as needed for partner-facing collaboration and project execution