AI Engineer

Company Overview

Tulong Tech AI builds cultural intelligence systems that help organizations turn cultural complexity into a strategic advantage. The company treats cultural intelligence as an important business dataset and develops AI-driven products that support global growth while staying respectful of diverse communities. The work is highly cross-functional, bringing together people with expertise in culture, product, and engineering to create practical tools for multicultural operations.

Role Summary

This position is for an AI Engineer who will create the data backbone and core reasoning flow for the Cultural Intelligence Engine. You will work closely with cultural subject matter experts and the product manager, and this is a hands-on build-from-zero role where you will own implementation end to end.

Knowledge Infrastructure and Data Engineering

• Develop a cultural knowledge graph with PostgreSQL, pgvector, and Apache AGE to store and query structured relationships across more than 50 communities.
• Create ingestion and processing pipelines capable of handling varied cultural datasets at scale.
• Set up cultural annotation processes, including normalization, privacy safeguards, and knowledge synthesis.
• Design and tune retrieval-augmented generation pipelines so cultural information can be retrieved quickly and with low latency.
• Keep structured and unstructured cultural data accurate and consistently high in quality.

AI Reasoning and LLM Engineering

• Build multi-step LLM orchestration workflows using AWS Bedrock, including Claude and Cohere.
• Develop a multi-layer cultural validation flow that combines rules, LLM-based checks, and hybrid detection methods.
• Adapt multilingual transformer models such as HuggingFace BERT and Cohere/Aya for cultural appropriateness data.
• Embed foundation models into cultural analysis workflows.
• Create confidence scoring and uncertainty measures that decide when the system can approve automatically and when it should route to a human expert.

Collaboration and Documentation

• Partner with cultural subject matter experts to convert domain knowledge into structured training data.
• Join annotation quality reviews to make sure AI outputs match expert cultural assessment.
• Document architecture choices, data structures, and pipeline behavior in a clear and organized way.

Qualifications and Requirements

A bachelor's degree in Computer Science or a closely related engineering field is required. A master's degree or PhD in a relevant discipline is a strong advantage. You should have at least 2 years of professional AI/ML engineering experience, along with a proven record of launching AI or LLM-based products. Practical NLP experience is expected, including areas like text classification, entity recognition, language modeling, and modern NLP tooling. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn, plus experience with deployment environments, is beneficial. Exposure to data engineering or MLOps practices such as deployment, monitoring, and CI/CD is also an asset. Interest in cultural intelligence, ethical AI, and technology that serves diverse communities is highly valued.

Additional Information

This role is based onsite in Toronto, Ontario, Canada and is offered as a contract position.

Employment Details

No salary or stipend amount was provided. No number of openings was specified. Start date, duration, and application deadline were not stated.