Senior AI/ML Engineer (AI Lead) - Cyprus

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Tickmill is looking to hire a Senior AI/ML Engineer (AI Lead) to join our rapidly expanding team. The ideal candidate will be a highly hands-on and business-oriented professional, capable of designing and delivering production-grade AI systems that drive measurable business impact.

About Tickmill.

Tickmill is an award-winning, multi-regulated broker offering access to a broad range of asset classes, including CFDs on Forex, Stocks, Indices, Commodities, Cryptocurrencies, and Bonds, as well as Exchange Traded Derivatives like Futures & Options).

Founded in 2014, the Tickmill Group employs over 330 professionals across offices in London, Cyprus, Poland, Estonia, Seychelles and several other locations worldwide.  

Our culture is built on trust, transparency, and high standards.  We bring together ambitious professionals who are looking for an environment that supports them to dominate in their fields.   

Our diverse, multilingual teams are focused on innovation, delivering bold excellence, and raising the bar wherever we can.  

We offer competitive benefits packages, frequent team initiatives, and opportunities for professional growth. 
Join the Tigers!

What does the role look like?

The Senior AI/ML Engineer (AI Lead) will have the chance to:

• Design, develop, and productionise machine learning models end-to-end (training, validation, deployment, monitoring, retraining), ensuring reliability in real-world environments.
• Lead the development of AI use cases such as client lifetime value (CLV), churn prediction, and fraud/abuse detection, with clear alignment to business outcomes and measurable impact.
• Build and establish robust MLOps practices, including model deployment pipelines, CI/CD, environment promotion (dev/stage/prod), and lifecycle management.
• Implement model monitoring frameworks to track performance, data drift, data quality, and business impact, with clear retraining and escalation strategies.
• Ensure model explainability and transparency using techniques such as SHAP, feature attribution, and other interpretability methods appropriate for business-critical and regulated contexts.
• Define and enforce best practices around model governance, documentation, versioning, and auditability, proportional to model risk and business impact.
• Collaborate closely with Data Engineering to ensure high-quality data pipelines, feature engineering, reproducibility, and scalable data foundations.
• Work cross-functionally with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions, balancing speed and robustness.
• Drive continuous improvement through feedback loops, monitoring insights, and model retraining strategies, rather than one-off model delivery.
• Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery.

What do you need to succeed in this role?

• 5–8+ years of experience building and deploying machine learning models in production environments (not just experimentation).
• Strong Python programming skills and solid software engineering fundamentals (testing, code quality, modular design, maintainability).
• Strong understanding of machine learning concepts, model evaluation, feature engineering, and practical considerations in production systems (e.g. data leakage, drift, stability).
• Hands-on experience with large-scale data processing (Spark / PySpark).
• Experience with ML lifecycle tools (MLflow or similar) for experiment tracking, model management, and reproducibility.
• Experience building and maintaining CI/CD pipelines (GitHub Actions preferred) for ML or data workflows.
• Strong SQL skills and experience working with large, complex datasets in real-world environments.
• Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements.
• Experience working with model deployment, monitoring, drift detection, and retraining strategies in production systems.
• Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders, translating trade-offs clearly.
• Ability to operate effectively in environments with evolving requirements, imperfect data, and delivery pressure, balancing MVP speed with production robustness.

The below are considered as a plus:

• Experience in fintech, trading, or financial services environments, particularly where models influence business-critical decisions.
• Experience with real-time or streaming ML systems.
• Familiarity with modern AI approaches such as LLMs, embeddings, or retrieval-augmented generation (RAG), particularly where applied to business workflows or integrated with structured data.
• Experience working in regulated environments and implementing model governance frameworks (e.g. auditability, explainability, approvals, documentation standards).
• Experience contributing to team standards, mentoring, or leading applied AI delivery.

 By joining us, you can expect:    

• A Unique Opportunity for a career in a global, fast-growing company. 

• Attractive remuneration package based on qualifications and experience (including 13th salary and Discretionary Bonuses to reward exceptional performance).  

• Opportunities to learn and grow through our “Employee Training & Development program”. 

• Medical Insurance Cover, which includes Outpatient, Inpatient, and Dental Care. 

• Multiple events to bond with the team and the group through Quarterly/Semestrial Team Activities for all the Company. 

• Participation in our welfare investment and savings plan through our Provident Fund Scheme. 

• Birthday and Loyalty benefits. 
• Collaboration with SportBenefit.

What to expect from our recruitment process:

1. First interview with hiring managers or an HR call
2. Task Assessment
3. Technical Interview or home assignment
4. Final interview with top management 

Make your next Career step and apply NOW!   

*Due to the great number of applications, we receive for each of our open vacancies, we are unable to respond on an individual basis.