About the role
Company Description: Tulong transforms cultural complexity into a competitive advantage for organizations operating in a globally connected world. The company focuses on cultural intelligence as a critical yet often overlooked dataset in modern business. By building systematic cultural intelligence datasets and AI-powered solutions, Tulong helps organizations succeed globally while authentically serving diverse communities. Team members collaborate across disciplines to create tools that make multicultural operations more effective, inclusive, and sustainable. Joining Tulong means contributing to technology that aligns business outcomes with cultural understanding.
Role Description: We're looking for an AI Engineer to build the data foundation and core reasoning pipeline of our Cultural Intelligence Engine. You'll be working directly with cultural subject matter experts and the product manager. This is a hands-on, build-from-scratch role. You own the implementation.
Responsibilities: Knowledge Infrastructure and Data Engineering Design and build a cultural knowledge graph using PostgreSQL, pgvector and Apache AGE to store and retrieve structured cultural relationships across 50+ communities; Build data ingestion and processing pipelines that handle diverse cultural datasets at scale; Implement cultural annotation workflows including normalization, privacy controls and knowledge synthesis; Build and optimize RAG (Retrieval Augmented Generation) pipelines for low-latency cultural knowledge retrieval; and Maintain data quality standards across structured and unstructured cultural datasets. AI Reasoning and LLM Engineering Architect and build multi-stage LLM orchestration workflows using AWS Bedrock (Claude, Cohere); Implement a multi-stage cultural validation pipeline combining rule-based, LLM-based and hybrid detection approaches; Fine-tune multilingual transformer models (HuggingFace - BERT, Cohere/Aya) on cultural appropriateness datasets; Integrate foundation models into cultural analysis workflows; and Build confidence scoring and uncertainty quantification systems that determine when the AI is confident enough to auto-approve versus when to escalate to a human expert. Collaboration Work directly with cultural subject matter experts to translate cultural knowledge into structured training data; Participate in annotation quality reviews to ensure AI outputs align with expert cultural judgments; and Document architecture decisions, data schemas and pipeline logic clearly.
Required Skills Python: 2-4 years professional experience in Python-based AI/ML development; LLM Pipelines: Hands-on experience building and shipping LLM-based systems using LangChain, LangGraph or equivalent frameworks; NLP and Transformers: Solid HuggingFace Transformers experience - you understand what's happening inside the model, not just how to call an API; Data and Graph Databases: Experience with vector databases, relational databases or graph databases (PostgreSQL, pgvector or similar); Cloud Deployment: GCP, AWS or Azure experience - comfortable with Docker and cloud-native deployment; and NLP Fundamentals: Strong grasp of tokenization, embeddings, attention mechanisms and multilingual model behaviour.
Qualifications Bachelor's degree in Computer Science or a related engineering discipline is required Master's or PhD in a relevant field is considered a strong asset Minimum 2 years of professional experience in AI/ML engineering Demonstrated track record of building and shipping AI or LLM-based systems Hands-on experience with Natural Language Processing (NLP), such as text classification, entity recognition, language modelling, and modern NLP frameworks. Familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and cloud or on-prem infrastructure for AI deployment. Experience with data engineering or MLOps, including model deployment, monitoring, and CI/CD, is an asset. Interest in cultural intelligence, ethical AI, and building technology that supports diverse communities is highly beneficial.
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About the role
Company Description: Tulong transforms cultural complexity into a competitive advantage for organizations operating in a globally connected world. The company focuses on cultural intelligence as a critical yet often overlooked dataset in modern business. By building systematic cultural intelligence datasets and AI-powered solutions, Tulong helps organizations succeed globally while authentically serving diverse communities. Team members collaborate across disciplines to create tools that make multicultural operations more effective, inclusive, and sustainable. Joining Tulong means contributing to technology that aligns business outcomes with cultural understanding.
Role Description: We're looking for an AI Engineer to build the data foundation and core reasoning pipeline of our Cultural Intelligence Engine. You'll be working directly with cultural subject matter experts and the product manager. This is a hands-on, build-from-scratch role. You own the implementation.
Responsibilities: Knowledge Infrastructure and Data Engineering Design and build a cultural knowledge graph using PostgreSQL, pgvector and Apache AGE to store and retrieve structured cultural relationships across 50+ communities; Build data ingestion and processing pipelines that handle diverse cultural datasets at scale; Implement cultural annotation workflows including normalization, privacy controls and knowledge synthesis; Build and optimize RAG (Retrieval Augmented Generation) pipelines for low-latency cultural knowledge retrieval; and Maintain data quality standards across structured and unstructured cultural datasets. AI Reasoning and LLM Engineering Architect and build multi-stage LLM orchestration workflows using AWS Bedrock (Claude, Cohere); Implement a multi-stage cultural validation pipeline combining rule-based, LLM-based and hybrid detection approaches; Fine-tune multilingual transformer models (HuggingFace - BERT, Cohere/Aya) on cultural appropriateness datasets; Integrate foundation models into cultural analysis workflows; and Build confidence scoring and uncertainty quantification systems that determine when the AI is confident enough to auto-approve versus when to escalate to a human expert. Collaboration Work directly with cultural subject matter experts to translate cultural knowledge into structured training data; Participate in annotation quality reviews to ensure AI outputs align with expert cultural judgments; and Document architecture decisions, data schemas and pipeline logic clearly.
Required Skills Python: 2-4 years professional experience in Python-based AI/ML development; LLM Pipelines: Hands-on experience building and shipping LLM-based systems using LangChain, LangGraph or equivalent frameworks; NLP and Transformers: Solid HuggingFace Transformers experience - you understand what's happening inside the model, not just how to call an API; Data and Graph Databases: Experience with vector databases, relational databases or graph databases (PostgreSQL, pgvector or similar); Cloud Deployment: GCP, AWS or Azure experience - comfortable with Docker and cloud-native deployment; and NLP Fundamentals: Strong grasp of tokenization, embeddings, attention mechanisms and multilingual model behaviour.
Qualifications Bachelor's degree in Computer Science or a related engineering discipline is required Master's or PhD in a relevant field is considered a strong asset Minimum 2 years of professional experience in AI/ML engineering Demonstrated track record of building and shipping AI or LLM-based systems Hands-on experience with Natural Language Processing (NLP), such as text classification, entity recognition, language modelling, and modern NLP frameworks. Familiarity with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and cloud or on-prem infrastructure for AI deployment. Experience with data engineering or MLOps, including model deployment, monitoring, and CI/CD, is an asset. Interest in cultural intelligence, ethical AI, and building technology that supports diverse communities is highly beneficial.