Job Summary

We are looking for a Senior AI Engineer to take technical ownership of the design, development and productionisation of AI and machine-learning capabilities within a B2B procurement product. This is a hands-on role for someone who can move confidently from ambiguous business problems and imperfect data to reliable, measurable AI solutions.

You will lead the development of models and intelligent workflows across use cases such as supplier scoring, spend analytics, anomaly detection, forecasting, recommendations and document intelligence. You will work across the full lifecycle: problem definition, data preparation, modelling, evaluation, deployment, monitoring and ongoing improvement.

As a senior member of the team, you will set technical direction, raise engineering standards and mentor other engineers. You will be expected to make sound trade-offs between accuracy, explainability, latency, cost and maintainability, ensuring that AI features create meaningful value for enterprise users.

Key Responsibilities

- Own the end-to-end architecture and delivery of production-grade AI and machine-learning solutions for procurement and spend-management workflows.
- Partner with product, domain and engineering stakeholders to translate business problems into well-defined AI use cases, measurable objectives and pragmatic delivery plans.
- Design, develop and optimise models for supplier scoring, classification, anomaly detection, forecasting, recommendations and document intelligence.
- Build robust data-preparation and feature-engineering pipelines for structured, semi-structured and document-based data.
- Develop and evaluate predictive models using appropriate statistical and machine-learning approaches, including baseline definition, validation strategy and error analysis.
- Lead the use of LLMs, retrieval-augmented generation (RAG) and agentic AI workflows where they are suitable, safe and commercially valuable.
- Design reliable document-intelligence workflows involving OCR, information extraction, classification, validation and human-in-the-loop review.
- Establish model evaluation, experiment-tracking and versioning practices to ensure repeatable, auditable development.




- Deploy models and inference services that meet product expectations for performance, reliability, security and scalability.
- Define and implement model monitoring for quality, drift, latency, cost and operational health; lead retraining and improvement initiatives when required.
- Work with backend and platform engineers to integrate AI capabilities into product APIs, workflows and user experiences.
- Review code, technical designs and model choices; establish high standards for testing, documentation, reproducibility and production readiness.
- Mentor junior engineers and contribute to team capability through knowledge sharing, pairing and constructive technical feedback.
- Stay current with relevant advances in machine learning, LLM applications and MLOps, applying new approaches selectively and responsibly.

Requirements

Mandatory Requirements

- 5+ years of professional experience in AI, machine learning, data science or a closely related engineering role, including experience delivering production systems.
- Strong proficiency in Python and practical experience with the core data and machine-learning ecosystem, including pandas, NumPy and scikit-learn.
- Solid experience designing, training, tuning and evaluating machine-learning models, including cross-validation, hyperparameter optimisation and error analysis.
- Demonstrated experience with tree-based and gradient-boosting models such as XGBoost, LightGBM or equivalent approaches.
- Strong SQL skills and hands-on experience working with complex, imperfect and high-volume business data.
- Proven expertise in data cleaning, validation, feature engineering and building repeatable data-processing pipelines.
- Experience delivering one or more of the following in production: anomaly detection, forecasting, recommendation systems, classification or document intelligence.
- Practical experience with LLM-based applications,



including prompt design, RAG patterns, tool/function calling or agentic workflows.
- Experience deploying and maintaining model inference services or AI-enabled APIs in collaboration with software engineering teams.
- Strong understanding of model performance, explainability, bias, data leakage, model drift and the trade-offs involved in production AI systems.
- Experience with experiment tracking, model versioning, reproducibility and monitoring practices.
- Strong software-engineering fundamentals, including API integration, testing, code review, Git and maintainable production code.
- Ability to independently lead technical discussions and communicate complex AI concepts clearly to product, engineering and non-technical stakeholders.

Good To Have

- Experience with deep-learning frameworks such as PyTorch, TensorFlow or Keras.
- Experience with NLP, OCR, information extraction and document-processing pipelines.
- Experience with time-series forecasting libraries and methods, such as statsmodels, Prophet or equivalent.
- Familiarity with LangChain, LlamaIndex or similar frameworks for LLM application development.
- Experience with MLOps platforms, model serving, feature stores, workflow orchestration or CI/CD for machine-learning systems.
- Experience deploying on AWS, GCP or Azure.
- Exposure to procurement, supply-chain, finance, enterprise SaaS or other data-rich B2B product domains.

What We Care About

- Building AI capabilities that solve real user and business problems rather than pursuing novelty for its own sake.
- Sound engineering judgement: choosing the simplest reliable solution that delivers measurable value.
- Ownership from discovery through production operation, not only model development.
- Rigour around data quality, evaluation, security, explainability and responsible AI use.
- Clear, direct communication and productive collaboration across technical and non-technical teams.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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