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Founding AI Engineer
Senior AI/ML Engineer – LLM & Agentic Workflow Systems
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About the Role
We’re building next-generation AI systems for business decision-making. Your mission will be to design, develop, and operationalise reliable, production-grade LLM and agentic workflows that turn data into actionable insights.
Key Responsibilities
- Design and build production-grade LLM and agentic workflows for decision support.
- Turn customer, market, and operational data into structured reasoning contexts for AI models.
- Develop evaluation, tracing, replay, and quality-control systems for nondeterministic AI outputs.
- Work on counterfactual analysis, forecasting, customer behaviour modelling, and decision support.
- Improve reliability through structured outputs, retrieval-augmented generation (RAG), constraints, calibration, testing, and model comparisons.
- Architect internal tools that enable repeatable AI workflows across customers and use cases.
- Help define measurements for predictive utility, accuracy, and trustworthiness.
- Collaborate directly with founders on product strategy, customer challenges, and technical architecture.
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What We’re Looking For
A strong fit would likely have:
- 5+ years of engineering experience.
- Proficiency in Python and a knack for building production-scale systems.
- A background in ML, data science, or applied AI.
- Hands-on experience with LLMs, agents, RAG, tool use, or AI workflows.
- Experience designing evaluations, tests, metrics, or quality gates for AI/ML systems.
- Judgement to manage hallucinations, uncertainty, failure modes, and validation strategies.
- Comfort working with real-world datasets, SQL, APIs, and data pipelines.
- Ability to debug both code and AI reasoning.
- Strong product intuition: You should care whether outputs actually help users.
- Autonomy in ambiguity: Thrives in unstructured or evolving environments.
- Clear communication and integrity—prioritising accuracy over impression.
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Especially Relevant Experience
We’d love to see prior work in:
- Production LLM applications or agentic systems.
- Evaluation frameworks for LLMs or ML models.
- Applied ML for forecasting, experimentation, analytics, or decision intelligence.
- RAG, embeddings, vector search, structured extraction, or model orchestration.
- Statistical analysis, A/B testing, causal inference, or model calibration.
- Data products requiring traceability, auditability, or reliability.
- Early-stage startups where you owned technical vision and implementation.
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