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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building AI-enabled tools and backend services, with a strong focus on LLM applications, API integration, and data-driven decision-making. Proficient in evaluating AI outputs and ensuring quality through robust testing and collaboration with multidisciplinary teams.
Highest-signal resume keywords
LLM Application DevelopmentAPI IntegrationSQL ProficiencyPython ProgrammingCI/CD Experience
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 EngineeringData EngineeringMachine LearningPrompt DesignStructured OutputsEmbeddingsSemantic SearchRanking MetricsTesting DisciplineHuman Review Design
Soft Skills
Strong Communication SkillsCollaborative ApproachProblem-Solving
Tools & Technologies
Microsoft AzureElasticsearchKafkaCloud InfrastructureData Pipelines
Industry Keywords
Retail SearchEcommerce SearchProduct TaxonomyA/B TestingClickstream Data
Tech Stack
Tools & technologiesAzureCloudElasticSearchKafkaPythonSQL
About the role
Key responsibilities & impact- Build AI-enabled tools that help Search teams find problems, test changes and measure relevance more quickly
- Design and build AI-enabled features that turn search behaviour, diagnostics and quality data into prioritised, actionable insights
- Build backend services and APIs for tools that collect and combine human relevance judgements
- Turn the existing offline evaluation tool into on-demand environments that developers can run in the CI/CD pipeline
- Define and automate checks on the accuracy and reliability of AI features, including LLM-based relevance judgements
- Work with product, design, data science and Search colleagues to turn prototypes into production releases
- Integrate with existing client platforms and APIs
- Monitor quality, latency and cost, and document decisions and trade-offs
- Share work early through documentation, demonstrations and incremental pull requests
- Own deliverables within agreed architecture and engineering practices
- Communicate decisions, risks and trade-offs clearly
- Assess the quality of AI-generated outputs with Search and data science colleagues
- Experiment quickly, test assumptions against real data and iterate using evidence
- Balance exploration with delivery and avoid unnecessary complexity early on
- Design for trust, clarity and adoption by tool users
- Recognise when deterministic rules, traditional machine learning or human review are more appropriate than an LLM
Requirements
What you’ll need- Five or more years of professional experience across software engineering, data engineering, machine learning or applied AI
- Hands-on experience building LLM applications in production, including prompt design, structured outputs and API integration
- Understanding of embeddings, semantic search and hybrid retrieval, and their trade-offs against lexical search
- Ability to compare models and approaches on quality, latency, security and cost
- Experience building offline evaluations and benchmarks with labelled datasets, and using LLMs as judges calibrated against human labels
- Understanding of ranking metrics such as nDCG, Precision at K and MRR, and of inter-rater agreement
- Awareness of hallucination, bias, drift and privacy risks, and experience designing human review
- Strong SQL skills and experience with large analytical datasets
- Familiarity with medallion architectures and integrating AI with data pipelines and APIs
- Strong Python skills, including maintainable backend services and APIs
- Good testing discipline across application, data and AI components
- Experience with CI/CD, containerisation and cloud infrastructure, ideally Microsoft Azure
- Demonstrated experience delivering AI-enabled systems beyond isolated proofs of concept
- Proven ability to ship iterative, user-focused solutions with appropriate attention to reliability and safety
- Strong written and verbal communication skills, including explaining technical choices and evaluation results to non-technical stakeholders
- A collaborative approach to multidisciplinary product delivery
- Nice to have: Experience in retail or ecommerce search, including product taxonomy and long-tail queries
- Nice to have: Hands-on experience with Elasticsearch or another production search platform, or with a vector database
- Nice to have: Experience building offline evaluation or A/B testing tooling for search or recommendation
- Nice to have: Experience building human annotation or rating tools
- Nice to have: Experience with event streaming, such as Kafka, or clickstream and web analytics data
Benefits
Comp & perks- Highly competitive compensation package
- Medical insurance
- Flexible remote work
- Opportunity to join an organization with triple-digit growth
- Opportunity to form part of a multicultural community of tech experts