Gen AI Engineer

Innovation & GenAI Integration

• Explore opportunities to embed Generative AI into data and analytics workflows.
• Evaluate and integrate APIs or LLM frameworks for enhanced data discovery, summarization, or predictive analytics.
• Contribute to internal best practices for responsible and scalable AI adoption.

Prototype Development

• Design and build quick, functional prototypes using Builder.io, Databricks, Python, and AI/ML APIs (e.g., OpenAI, Anthropic, Hugging Face).
• Translate product ideas into clickable or working demos to validate feasibility and customer value.
• Lead early POCs for new data or GenAI-driven products.

Data Engineering & Architecture

• Architect and develop scalable ETL pipelines and data transformations using Databricks, PySpark, SQL, and cloud data platforms (Azure/AWS/Google Cloud Platform).
• Partner with data engineers to ensure prototypes can evolve into production-grade systems.
• Understand data modeling, governance, and integration patterns across large-scale data ecosystems.

Cross-functional Collaboration

• Work closely with Product Managers to translate business needs into technical requirements and prototypes.
• Partner with Engineering Leads to ensure alignment on technical feasibility and architectural direction.
• Engage with customers and internal stakeholders to refine requirements and gather feedback.

Leadership & Influence

• Mentor junior team members in data engineering and prototype development.
• Help define standards for prototyping, data architecture, and technical collaboration.
• Drive a culture of experimentation, rapid iteration, and customer-centric design.

Good to have:

Required Technical Skills:

• Strong hands-on skills in Databricks, PySpark, SQL, and cloud data platforms (Azure/AWS/Google Cloud Platform).
• Experience designing ETL pipelines, data workflows, and data modeling.
• Proficiency in Python for both data engineering and AI/LLM-based solutions.
• Familiarity with Builder.io or similar low-code / front-end prototyping tools.
• Exposure to LLM APIs, prompt engineering, or AI-driven app integrations.
• Experience building and testing prototypes end-to-end from concept to user validation.

Preferred:

• Previous experience in customer-facing technical roles (e.g., Solutions Architect, Technical Product Manager).
• Understanding of MLOps, vector databases, or retrieval-augmented generation (RAG) patterns.
• Ability to communicate complex technical ideas clearly to executives, PMs, and customers.