Description

You will own the architecture and implementation of advanced, data-driven AI solutions.

Responsibilities

• Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems.
• Build and integrate cloud-native data pipelines using Snowflake, Airflow, and Vertex AI.
• Implement retrieval-augmented generation (RAG) pipelines and multimodal data solutions.
• Define and promote engineering standards balancing agility, scalability, and governance across AI/data workflows.

Required Skills

• 10+ years of experience in enterprise data architecture or engineering with a focus on AI and cloud-native data platforms.
• Proven experience designing and optimizing large-scale AI systems, including LLM and GenAI 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.
• Proficiency with Docker and Kubernetes.

Preferred Skills

• Experience with agentic AI design patterns, including tool-use orchestration or autonomous workflow agents.
• Demonstrated ability to rapidly prototype new AI concepts and transition PoCs into production-grade systems.

Key Skills

Education

Bachelor's or Master's degrees