Principal Architect

Responsibilities

• 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.

• 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.

• 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.

• 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.

• 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, Lang Chain, 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.

• Experience with agentic AI design patterns, including tool‑use orchestration, autonomous workflow agents, or AI copilots.
• Proficiency in API design, microservices, and containerization (Docker, Kubernetes).
• Demonstrated ability to rapidly prototype new AI concepts and transition successful PoCs into production‑grade systems.

Role

Description:

GenAI, LLMs, RAG pipelines, vector databases

Python SQL (expert level)
Cloud-native data platforms (Snowflake, Airflow, Vertex AI AWS Azure)
Modern AI frameworks (Lang Chain, Hugging Face, PySpark)
Data modeling cloud architecture

Building AIML pipelines end-to-end Hands‑on prototyping and product ionizing AI solutions

Data Architecture and Modeling