The posting
About StarCompliance
StarCompliance is a leading provider of employee compliance technology, trusted by more than 350 global financial institutions and highly regulated organisations. Its cloud-native SaaS platform helps organisations manage regulatory obligations, monitor employee activity, identify potential conflicts of interest and mitigate risk.
StarCompliance is investing in the next generation of its AI-enabled platform. This role will play a pivotal part in building the data foundations, AI capabilities and engineering practices that enable smarter, more predictive compliance solutions.
The Role
We are looking for a hands-on technical leader who has personally built modern data platforms and production AI solutions, and can lead a team that delivers high-quality software at significantly greater speed through AI-accelerated engineering.
This is not a purely strategic leadership position. You will remain technically involved, writing and reviewing code, shaping architecture, building proofs of concept and making key technical decisions. You will lead the development of a robust data platform, deliver production-grade Generative AI capabilities, and provide technical direction for traditional machine learning initiatives, including risk scoring and predicting decisions from historical patterns.
You will also be responsible for transforming how the team works by embedding AI across the software delivery lifecycle, from requirements and coding through to testing, code review, documentation and operations. Success will be measured not simply by AI tool adoption, but by demonstrable improvements in engineering productivity, delivery speed and quality.
Working closely with the CTO, Product Director, Engineering, Architecture and Security teams, you will help define and deliver StarCompliance’s technical data and AI roadmap while building a high-performing team.
Responsibilities:
- Personally design and build the data platform that underpins StarCompliance’s AI and analytics capabilities, covering data ingestion, transformation, storage, warehousing or lake architecture, and data modelling.
- Establish reliable, scalable and well-governed data pipelines that make high-quality data accessible for AI and machine learning applications.
- Define data architecture and engineering standards, ensuring data quality, lineage, security and governance are embedded from the outset.
- Develop and maintain the foundations required to train, deploy, monitor and improve models in production.
- Make hands-on technical contributions rather than operating solely through delegation or programme oversight.
- Personally build and deploy Generative AI capabilities into production, including LLM-powered applications, retrieval-augmented generation (RAG), AI agents and other relevant AI product features.
- Establish robust evaluation frameworks to measure the accuracy, relevance, reliability and safety of AI outputs.
- Define how AI solutions are tested, monitored and improved, including the management of hallucinations, latency, cost and production performance.
- Evaluate emerging AI technologies and make informed decisions about when to build, buy or integrate solutions.
- Ensure AI capabilities deliver measurable customer and business value while meeting the security, privacy and governance requirements of a regulated environment.
- Provide technical direction for supervised machine learning initiatives, particularly risk scoring, classification and predicting decisions based on historical patterns.
- Lead data scientists in selecting appropriate modelling approaches and evaluating their suitability for real-world business problems.
- Understand and address practical challenges such as limited or imbalanced labelled data, model calibration, explainability, bias, validation and performance monitoring.
- Establish standards for model evaluation, deployment, monitoring, drift detection and ongoing improvement.
- Ideally, bring direct experience of personally building and deploying supervised scoring or classification models in production, such as credit risk, fraud detection, propensity modelling or churn prediction.
- Transform how the engineering team builds and delivers software by embedding AI throughout the development lifecycle.
- Implement effective AI-assisted workflows using tools such as Claude, Codex, Cursor or equivalent technologies, supported by reusable agents, skills, standards and engineering practices.
- Integrate AI into requirements analysis, implementation, code review, automated testing, documentation, debugging and operational support.
- Establish practical guardrails for AI-generated code, including quality, security, maintainability and human review.
- Measure the impact of these practices through evidence such as reduced cycle times, faster delivery, improved test coverage and reduced rework.
- Build a culture where AI is an integral part of how the team delivers, rather than simply a collection of optional tools used by individual engineers.
- Lead, mentor and develop a high-performing Data & AI team, with direct management responsibility for team members.
- Remain actively involved in coding, architecture reviews, technical design and proof-of-concept development.
- Set clear technical standards and create an environment that balances engineering rigour with experimentation and rapid delivery.
- Lead and develop data engineers, ML engineers and data scientists, providing the appropriate technical direction across each discipline.
- Work closely with Product and Engineering leadership to prioritise initiatives, resolve technical challenges and deliver against business objectives.
- Communicate complex technical decisions clearly to both technical and non-technical stakeholders.
- Ensure data and AI solutions are designed for a highly regulated environment, with appropriate controls, transparency, traceability and accountability.
- Establish proportionate model governance and risk management practices for production AI and machine learning systems.
- Align technical investment with customer needs, commercial priorities and the wider StarCompliance product strategy.
- Partner with the CTO and Product Director to define priorities, assess outcomes and demonstrate the value delivered by the Data & AI function.
Skills and Experience
- Proven experience personally building a modern data platform, including ingestion pipelines, data transformation, warehousing or lake architecture, and data modelling.
- Strong Python and SQL skills, with the ability to write production-quality code and work directly with data and engineering systems.
- Experience designing and implementing scalable, reliable data pipelines and production data architectures.
- Demonstrable experience personally building and deploying Generative AI or LLM-powered features into production.
- Practical experience with RAG, AI agents or comparable LLM application architectures.
- Experience designing and implementing evaluation methods to measure the quality, reliability and performance of production AI systems.
- Strong understanding of supervised machine learning and the ability to challenge, guide and direct data scientists.
- Ability to make sound technical decisions around model selection, scarce or imbalanced labels, calibration, explainability and ongoing monitoring.
- Understanding of how predictive models are validated, deployed and maintained in production.
- Demonstrable experience using AI to improve the engineering output of a team, with evidence of measurable improvements in delivery speed, productivity or quality.
- Experience embedding AI-assisted practices across multiple stages of the development lifecycle, rather than simply introducing coding assistants.
- Ability to establish reusable agents, skills, workflows or standards that are actively adopted by the team.
- Strong software engineering fundamentals, including code review, automated testing, architecture and engineering quality controls.
- Proven experience leading a technical team with direct reports.
- Evidence of remaining hands-on in a recent role through coding, architecture decisions, technical reviews or building production solutions.
- Experience setting technical direction, developing engineers and delivering production systems.
- Production predictive modelling: Personally built and deployed supervised scoring or classification models, particularly in credit risk, fraud detection, propensity modelling, churn prediction or similar use cases.
- Cloud and data platforms: Experience with Microsoft Azure and/or Snowflake.
- Regulated industries: Experience in financial services, banking, insurance, fintech or regtech.
- Model governance: Familiarity with model risk management, explainability, auditability and governance in regulated environments.
- Production operations: Experience with MLOps, CI/CD, automated model deployment, monitoring and model performance management.
What Does Success Look Like?
- A reliable, scalable data platform is in place, built on sound engineering principles and capable of supporting production AI and machine learning.
- Generative AI capabilities are deployed into production, with robust evaluation and monitoring demonstrating their effectiveness.
- The team can confidently develop and maintain traditional predictive models, including risk scoring and decision prediction.
- AI is embedded throughout the engineering lifecycle, supported by team-wide workflows and measurable improvements in delivery speed and quality.
- A technically strong, accountable team is established, with clear leadership, high engineering standards and the ability to deliver meaningful business outcomes.
