- Work across backend, frontend, cloud, and AI developing RAG/LLM solutions, using data insights, and ensuring high-quality, reliable code delivery.

What You'll Do :

Responsibilities :

Business :

- Apply domain knowledge of commercial operations to technical solutions.

- Bridge business and technology conversations fluently, speaking the domain language naturally.

- Shadow operations to build understanding and make better technical decisions by understanding broader business context.

Delivery :

- Deliver working solutions rapidly days not weeks.

- Use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality.

- Deliver complete features end-to-end independently across frontend, backend, database, and infrastructure.

Generative AI :

- Design production RAG systems with appropriate chunking, embedding, and retrieval strategies.

- Optimise for relevance and latency, handle edge cases, and evaluate end-to-end system quality.

- Design evaluation frameworks with custom evaluators tailored to your use case.

- Build golden datasets and run experiments to compare prompt and model changes systematically.

Data Science & Analytics :

- Perform exploratory data analysis to inform solution design and validate assumptions.

- Apply statistical methods to understand relationships in operational data and build predictive models where they add business value.

- Evaluate model performance, communicate analytical findings to non-technical stakeholders, and integrate data-driven insights into software solutions.

Documentation :

- Create comprehensive documentation for complex systems.

- Write precise specifications that enable accurate AI-generated code, establish documentation practices for your projects, and

ensure docs are discoverable.

- Identify patterns across implementations and propose candidates for generalisation.

Role Behaviours :

Own the Outcome :

- Take end-to-end ownership of features and business outcomes.

- Accept technical debt intentionally when it accelerates value delivery.

- Build trust through rapid delivery of working solutions.

- Own stakeholder relationships and balance quality with delivery speed.

- AI may generate the code, but responsibility for outcomes remains with you.

Be Polymath Oriented :

- Bridge gaps between engineering, design, business, data science, and the pharmaceutical domain.

- Rapidly immerse in new domains.

- Speak the language of Commercial operations and make better decisions by understanding the broader business context.

- See connections across disciplines that others miss.

Communicate with Precision :

- Separate requirements, designs, and tasks with precision.

- Enable AI to generate accurate code through clear specifications.

- Translate between technical and business language fluently.

- Facilitate productive discussions and reduce ambiguity in everything you communicate.

Working-level Skills :

Full-Stack Development :

- You deliver complete features end-to-end independently frontend, backend, database, and infrastructure.

- You make pragmatic technology choices and deploy what you build.

Architecture & Design :

- You design components and services independently for moderate -to-high complexity.

- You make appropriate trade-off decisions, document design rationale, and consider AI integration points in your designs.

Code Quality & Review :

- You produce consistently high-quality, well-tested code.

- You review AI-generated code critically and never ship code you don't fully understand.

- You identify edge cases and ensure adequate test coverage.

Problem Discovery :

- You navigate ambiguous problem spaces independently.

- You discover requirements through observation and user shadowing, re frame problems to find higher value solutions, and distinguish symptoms from root causes.

Rapid Prototyping & Validation :

- You deliver working solutions rapidly (days not weeks).

- You use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality.

Retrieval Augmentation :

- You design production RAG systems with appropriate chunking, embedding, and retrieval strategies.

- You optimise for relevance and latency, handle edge cases, and evaluate end-to-end system quality.

AI-Augmented Development :

- You integrate AI tools strategically into your development workflow.

- You review AI-generated code with the same rigour as human code and never ship code you don't fully understand.

Multi-Audience Communication :

- You present complex topics clearly to any audience, facilitate productive discussions, translate between technical and business language fluidly, and write compelling proposals and specifications.

Business Immersion :

- You apply deep domain knowledge to technical solutions, bridge business and technology conversations fluently, speak the domain language naturally, and shadow operations to build understanding.

Foundational-level Skills :

Stakeholder Management :

- You proactively update stakeholders on progress, handle basic expectation setting, and escalate concerns appropriately.

- You build rapport with regular collaborators and manage expectations around delivery timelines.

DevOps & CI/CD :

- You configure basic CI/CD pipelines, understand containerisation, and can troubleshoot common build and deployment failures.

Cloud Platforms :

- You deploy applications to cloud platforms and use common services (compute, storage, databases, queues).

- You understand cloud pricing and basic security configuration.

AI Evaluation & Observability :

- You instrument applications with tracing to capture execution flow.

- You create evaluation datasets from production data, run basic LLM-asjudge evaluations, and apply pre-built evaluators for common metrics like faithfulness and relevance.

Data Integration :

- You create simple data transformations and handle common data formats.

- You identify and report data quality issues and understand basic ETL concepts.

Data Analysis :

- You perform exploratory data analysis independently, create effective visualisations, and identify patterns in data.

- You ask good questions about data quality and context.

Statistical Modeling :

- You apply common statistical tests appropriately and interpret pvalues, confidence intervals, and effect sizes.

- You recognise when assumptions are violated.

Model Development :

- You implement standard ML pipelines (data prep, training, evaluation), evaluate model performance appropriately, and avoid common pitfalls like data leakage.

Awareness-level Skills :

Site Reliability Engineering :

- You understand SLIs, SLOs, and error budgets conceptually.

- You can use monitoring dashboards and escalate issues appropriately.

Data Modeling :

- You understand the difference between relational and non-relational data stores.

- You can create basic schemas from specifications with guidance.

AI Literacy :

- You understand basic AI concepts (training, inference, prompts) and can recognise AI-powered features in products.

- You know AI has limitations and when traditional approaches may be better.

Model Fine-Tuning :

- You understand fine-tuning concepts (transfer learning, domain adaptation) and when fine-tuning is appropriate versus using prompting or RAG.

- You can use fine-tuning APIs with guidance.

Synthetic Data Generation :

- You understand what synthetic data is and why it's used (privacy, availability, testing).

- You can use pre-generated synthetic datasets and recognise the difference between synthetic and real data.

What You Bring :

- Bachelor's degree in Computer Science, Software Engineering, Statistics, or related field with 7-10 years of relevant professional experience.

- Strong production experience with Python and JavaScript/TypeScript across backend and frontend.

- Hands-on experience with modern frontend frameworks (such as Next.js or React) and backend API development.

- Production experience with cloud platforms (AWS preferred; Azure or GCP also valued), including infrastructure-as-code tools (such as CloudFormation or Terraform).

- Working knowledge of multiple database paradigms, including relational databases (such as PostgreSQL), document databases, and key-value stores (such as Redis).

- Experience with CI/CD pipelines (such as GitHub Actions) and a strong understanding of path-to-production practices.

- Demonstrable fluency with AI coding tools (such as Claude Code, Cursor, GitHub Copilot, or similar) and experience building agentic engineering workflows.

- Hands-on experience building production generative AI applications (LLM integrations, vector databases, RAG systems) is essential.

- Experience with data analysis, statistical modeling, or machine learning in a professional context is required.

- This could include exploratory data analysis, building regression or classification models, A/B test design, or integrating ML predictions into software products.

- Familiarity with data science tooling (such as pandas, scikit-learn, or equivalent) and comfort working in Jupyter notebooks or similar environments.

- Experience navigating ambiguous problem spaces, working directly with business stakeholders and end users, and shipping working solutions rapidly is strongly valued.

- Experience in an embedded, forward-deployed, or consulting-style engineering model is a strong plus.