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.

Bachelors or Masters degree in Computer Science Engineering Data Science or related field.

Preferred :

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 databasesPython SQL (expert level)Cloud-native data platforms (Snowflake Airflow Vertex AI AWS Azure)Modern AI frameworks (LangChain Hugging Face PySpark)Data modeling cloud architectureBuilding AIML pipelines end-to-endHands-on prototyping and productionizing AI solutions

Competencies : Data Architecture and Modeling

Key Skills

Kubernetes,S3,Google Cloud Platform,Cassandra,System Architecture,Redshift,AWS,Cloud Architecture,NoSQL,UML,Kafka,Distributed Systems

Employment Type : Full Time

Experience : years

Vacancy : 1