Senior Data Solution / AI

Your mission is to lead a high-performing team of Data and AI Engineers to deliver engineering excellence.You will bridge the gap between complex algorithmic theory and operational reality, ensuring that our clients data ecosystems are not only scalable and secure but also drive genuine business impact through machine learning and advanced analytics.

Instead of a checklist of daily duties, here is what success looks like in your first 12 months:

• Engineering Excellence: You have successfully overseen the design and deployment of 2-3 end-to-end Data & AI platforms for enterprise clients, ensuring they are built to Google Cloud best practices and are technical debt-free.
• Pipeline Modernization: You have replaced legacy, manual data processing with automated, scalable ETL/ELT pipelines. By implementing modern data orchestration, you have significantly reduced data latency for critical business decision-making.
• Gen AI Productionization & LLMOps: You have moved Gen AI initiatives from experimental RAG (Retrieval-Augmented Generation) prototypes to robust, production-grade agentic environments
• Strategic Leadership: You have become a trusted advisor for client CTOs and Data Leads. You are regarded not just as a technical lead, but as a critical extension of their strategic team, helping them navigate the roadmap from data maturity to AI innovation.

We are looking for a technical powerhouse who combines logical precision with a collaborative leadership mindset.

The Must-Haves:

• Leadership Experience: 2-3+ years of experience leading Data Engineering or AI/ML teams, preferably within a consultancy or professional services environment.
• Data Engineering Mastery: Deep expertise in building large-scale data warehouses (BigQuery) and designing complex pipeline architectures (Dataflow, Pub/Sub, Airflow, Composer).
• Consultative Polish: The ability to translate data-speak into business value for executive stakeholders and the adaptability to solve problems in messy legacy environments.

The Nice-to-Haves:

• Certifications: Google Professional Data Engineer or Professional Machine Learning Engineer.
• Geospatial Knowledge: Having knowledge of combining geospatial dataset and logic behind geospatial analysis.
• Scripting Excellence: Mastery of Python for custom automation and model development.
• AI & Machine Learning Proficiency: Experience in developing and deploying ML models, specifically within Natural Language Processing (NLP) or Computer Vision. You understand how to leverage Vertex AI to accelerate the ML lifecycle.