Intelligence Solution Engineer - Quality

Integrate AI-driven pipelines with existing enterprise data ecosystems such as data warehouses, data lakes, and graph databases.

Implement solutions leveraging frameworks like LangChain, LangGraph, and Databricks for scalable AI deployment.

Design and maintain vector embedding databases, RAG (Retrieval Augmented Generation), and semantic retrieval systems for high-quality contextual understanding.

Apply graph-based reasoning using Neo4j to enable contextual intelligence and knowledge discovery.

Develop and optimize AI-driven applications using Python and Streamlit, ensuring seamless user interaction and robust functionality.

Design and implement AI workflows leveraging Databricks ML and MLOps.

Collaborate with stakeholders to build AI solutions, reducing redundancy and promoting standardization.

36 years of experience in Agentic AI solution engineering or intelligent systems development.

Proven experience with LangChain , LangGraph , or equivalent agentic AI frameworks.

Hands-on experience with Databricks , including Databricks ML and MLOps workflows.

Strong expertise in Python and Streamlit for building interactive and intelligent applications.

Experience integrating multimodal AI models and designing vector-based retrieval or RAG systems .

Solid understanding of Neo4j or similar graph databases for context graph reasoning.

Strong communication and collaboration skills for working with global, cross-functional teams.

Collaborates: Builds partnerships and works collaboratively with others to meet shared objectives.

Communicates Effectively: Develops and delivers multi-mode communications tailored to diverse audiences.

Customer Focus: Builds strong customer relationships and delivers customer-centric solutions.

Interpersonal Savvy: Relates openly and comfortably with diverse groups of people.

Data Analytics: Interprets and communicates complex data insights that drive business decisions.

Data Mining: Extracts insights using advanced data exploration and visualization techniques.

Data Modeling: Creates and validates data models using modern standards, ensuring compliance and accuracy.

Data Communication & Visualization: Tells compelling data stories through visualizations and dashboards.

Data Literacy: Understands and communicates data sources, methods, and applied techniques effectively.

Data Profiling & Quality: Identifies and corrects data flaws, ensuring high-quality and reliable information.

Values Differences: Recognizes and leverages the value that diverse perspectives and cultures bring.

This position may require licensing or clearance for compliance with export control or sanctions regulations.

Candidates must demonstrate both technical excellence and an applied understanding of AI-driven quality analytics.

Bachelors or Masters degree in Computer Science, Information Technology, or a related technical field.

Equivalent experience may be considered.