Principal Architect

Overview: 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. 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