Senior Data Engineer

• 5+ years leading technology strategy, engineering teams, and AI product delivery.
• 6+ years designing and deploying production-grade AI and machine-learning solutions.
• 5+ years with cloud platforms, DevOps, CI/CD, containerization, and data pipelines.
• Experience managing multidisciplinary teams of up to 10 people.
• Proven delivery of AI solutions within banking, financial services, healthcare, insurance, or real estate.
• Experience working directly with enterprise customers and senior stakeholders.
• Startup, co-founder, CTO, or technology-department-building experience is strongly preferred.

Ideal Experience Mix

• 5+ years leading technology strategy, engineering teams, and AI product delivery.
• 6+ years designing and deploying production-grade AI and machine-learning solutions.
• 5+ years with cloud platforms, DevOps, CI/CD, containerization, and data pipelines.
• Experience managing multidisciplinary teams of up to 10 people.
• Proven delivery of AI solutions within banking, financial services, healthcare, insurance, or real estate.
• Experience working directly with enterprise customers and senior stakeholders.
• Startup, co-founder, CTO, or technology-department-building experience is strongly preferred.

Education

• Double Degree in Mathematics and Computer Science, or an equivalent degree in Computer Science, Mathematics, Artificial Intelligence, Data Science, Engineering, or a closely related quantitative field.
• Strong academic record preferred.
• Professional fluency in English and Spanish.

Core Skills

Leadership and Strategy

• Technology strategy and annual planning
• Engineering and AI team leadership
• Agile project and resource management
• Technical recruitment, mentoring, and training
• Project budgeting, costing, pricing, and cash-flow forecasting
• Enterprise stakeholder and client management
• Information security and ISO 27001 compliance

Artificial Intelligence and Machine Learning

• Machine-learning model development and production deployment
• Predictive modelling, lead scoring, attribution, and collections optimization
• Generative AI and large language models
• Voice and WhatsApp AI agents
• AI workflow and proposal-generation agents
• Model training, validation, monitoring, and versioning
• Experiment tracking with MLflow
• TensorFlow, Keras, scikit-learn, OpenAI, Anthropic, Gemini, Grok, and Ollama

Software Engineering

• Advanced Python
• TypeScript and JavaScript
• PHP, HTML, and CSS
• FastAPI, Flask, and Django
• REST APIs and backend system design
• Automated testing with pytest
• Software-quality controls and SonarQube quality gates

MLOps, DevOps, and Cloud

• CI/CD architecture and implementation
• GitHub Actions and Google Cloud Build
• Docker and Kubernetes
• Argo Workflows and Apache Airflow
• AWS and Google Cloud Platform
• Cloud-native ETL pipelines
• Model deployment, monitoring, and production support
• Grafana and Prometheus

Data and Analytics

• PostgreSQL, Redis, Supabase, and SAS
• Power BI, Looker Studio, and data visualization
• Data ingestion and transformation pipelines
• Financial and operational reporting systems

Automation

• n8n and Make
• Cron jobs, event triggers, and workflow orchestration
• Hybrid low-code and custom-developed automation systems

Key Responsibilities

• Define and implement the annual technology roadmap in alignment with business objectives.
• Lead AI, machine-learning, software-engineering, data, DevOps, and MLOps initiatives.
• Design and deploy production AI solutions for enterprise customers.
• Develop predictive models, generative-AI applications, conversational agents, and intelligent automation systems.
• Establish scalable model-training, experiment-tracking, deployment, monitoring, and retraining processes.
• Design and maintain cloud-native architectures across AWS and GCP.
• Implement CI/CD pipelines, automated testing, code-quality controls, and secure development practices.
• Lead multidisciplinary teams using agile methodologies.
• Recruit, train, mentor, and develop technical talent.
• Prepare project plans covering scope, resources, timelines, costs, risks, and commercial pricing.
• Manage technical relationships with enterprise customers and communicate complex solutions to non-technical stakeholders.
• Ensure compliance with ISO 27001 and information-security requirements throughout the AI development lifecycle.
• Build internal systems for financial tracking, operational reporting, workflow automation, and proposal generation.
• Evaluate emerging AI technologies and determine their suitability for customer and internal applications.