Principal Architect

Description:

"Responsibilities

Advanced Data Solutions & Engineering

• Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems.

• Build and integrate cloud-native data pipelines using tools such as Snowflake, Airflow, and Vertex AI.

• Implement retrieval-augmented generation (RAG) pipelines and multimodal data solutions.

• Drive automation, observability, and performance optimization across AI and data workflows.

Innovation & Applied AI

• Lead initiatives to explore, validate, and scale emerging AI technologies.

• Translate research and prototypes into production-ready capabilities.

• Collaborate across teams to embed AI-driven insights and automation into business processes.

• Evaluate and shape next-generation AI trends, including agentic systems and autonomous workflows.

Technology Leadership & Best Practices

• Champion hands-on experimentation and rapid solution delivery while maintaining technical excellence.

• Define and promote engineering standards that balance agility, scalability, and governance.

• Collaborate with security, compliance, and governance partners to ensure responsible data and AI usage.

• Mentor engineers and architects in modern data and AI development practices.

Collaboration & Knowledge Sharing

• Act as a trusted advisor for business and technology leaders on data-driven innovation.

• Lead internal workshops and training sessions to accelerate AI adoption.

• Represent the organization in external forums, conferences, and publications focused on data and AI innovation.

Qualifications

• 10+ years of experience in enterprise data architecture or engineering, with a strong hands-on focus on AI and cloud-native data platforms.

• Proven experience in designing, implementing, and optimizing large-scale AI systems, including LLM-based, GenAI, and agentic AI applications.

• Expertise in Python, SQL, and modern data frameworks (e.g., PySpark, Airflow, Snowflake, LangChain, Hugging Face, Vertex AI, OpenAI)

• Strong background in data modelling, distributed systems, and cloud architecture (AWS, GCP, or Azure).

• Experience developing and deploying AI/ML/GenAI pipelines leveraging vector databases and RAG frameworks.

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.