Senior Data Scientist / AI Engineer

Requirements

• Open to the right fit (data science, ML, or AI expert),
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or related field (or equivalent experience),
• (Desirable) Hands-on experience with LangChain, LangGraph or other LLM-agent frameworks for building and orchestrating conversational or task-driven agents,
• 4+ years of professional experience in data engineering, ML engineering or backend development - ideally in a startup or agile environment,
• (Desirable) Familiarity with big-data ecosystems (Hadoop, Spark),
• Proficient in Python and familiar with libraries such as Pandas, NumPy, and Scikit-learn,
• (Desirable) Experience with geospatial/spatial-data tools and workflows (PostGIS, GeoPandas),
• Experience with database systems and data modelling,
• (Desirable) Practical knowledge of Databricks or similar unified analytics platforms,
• Understanding of RESTful API principles and basic asynchronous programming,
• (Desirable) Deep understanding of SDLC best practices, MLOps, agile methodologies and test-driven development,
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerisation (Docker) is advantageous,
• Basic knowledge of version control (Git) and CI/CD pipelines,
• Strong problem-solving abilities and a proactive mindset,
• Excellent communication skills and ability to work collaboratively in a team,
• Willingness to learn and adapt to new technologies and challenges,
• Strong analytical skills with the ability to approach challenges creatively and effectively

What the job involves

• Lead the design, development and maintenance of robust data pipelines and ETL processes for seamless, scalable data flow,
• Architect and optimise data storage solutions (relational, NoSQL, object-store) to support diverse analytics and ML workloads,
• Establish and enforce best practices around data modelling, quality, lineage and governance,
• Design, train, deploy and monitor production-grade ML models (classification, regression, recommendation, anomaly detection, etc.),
• Architect and build intelligent agents using modern frameworks to automate decision-making and workflow orchestration,
• Continually evaluate model performance and retraining strategies to ensure reliability and fairness,
• Lead the development of FastAPI-based microservices and APIs—ensuring they’re secure, observable, and capable of scaling to high throughput,
• Contribute to asynchronous, event-driven architectures and integrations with external systems,
• Mentor and coach junior members; conduct design reviews and drive code-quality standards,
• Partner with Product and Engineering to translate business needs into technical requirements and roadmaps,
• Evangelise data-driven, ML-powered solutions across the organisation,
• Stay at the forefront of data engineering, MLOps, agent frameworks and API development trends,
• Own technical debt backlogs, identify areas for refactoring, and drive automation of repetitive tasks

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