Kata.aiAI Engineer (Full-time, Remote)
Company Description
Kata.ai is an Indonesian Conversational Artificial Intelligence company with a focus on creating technology to enhance the understanding of human conversations, improving the way humans collaborate with machines. Kata.ai’s Natural Language Processing (NLP) technology powers MultiPurpose chatbots (virtual customer service / virtual friend) for major corporations in Indonesia across different kinds of industries such as Unilever (FMCG), Telkomsel (Telco), Bank BRI (Financial Services), and Alfamart (Retail).
The company’s proprietary Kata Bot Platform can be leveraged to create feature-rich chatbots on top of Kata.ai’s robust and scalable AI technology platform, ensuring company of any size can easily build their own chatbot on any messaging platform. With this platform, it is now possible for the business to focus on designing engaging interaction for their customers, while Kata.ai handles all the technology aspects of the chatbots.
Established in 2015, the company has become a trusted partner for major corporations such as Microsoft, Accenture, and Line. In 2020, the company received Series-B funding from TransPacific Technology Fund and MDI Venture.
Job Description
You will design, build, and deploy production-grade AI systems — including LLM-powered conversational agents, RAG pipelines, NLP workflows, and voice AI integrations — to deliver intelligent, reliable, and measurable AI solutions for enterprise clients across government, financial services, healthcare, and telecommunications sectors — so that Kata's clients can automate customer interactions at scale with high accuracy, low latency, and strong business impact.
Qualifications
Qualifications & Education :
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computational Linguistics, or related field
- Master's degree in AI/ML is a plus
- Relevant certifications (GCP AI/ML, DeepLearning.AI, etc.) are advantageous
Technical Skills :
- LLM Integration: OpenAI GPT-4o, Anthropic Claude, Google Gemini, or open-source models (LLaMA, Mistral, Qwen)
- AI Frameworks: LangChain, LlamaIndex, CrewAI, or similar agent/RAG orchestration frameworks
- Prompt Engineering: System prompt design, few-shot prompting, chain-of-thought, structured output (JSON mode, function calling)
- RAG Pipelines: Document chunking, embedding strategies, retrieval optimization, reranking
- Vector Databases: Pinecone, Weaviate, Qdrant, or pgvector
- Voice AI: LiveKit Agents SDK, STT integrations (Deepgram, Google Speech-to-Text, Whisper), TTS integrations (ElevenLabs, Google TTS)
- Languages: Python (required); FastAPI for AI service exposure
- Cloud: GCP or Azure for AI/ML workload deployment — Vertex AI, Azure OpenAI, Cloud Run
- Evaluation Frameworks: RAGAS, DeepEval, custom eval pipelines, or LLM-as-judge approaches
- Containerization: Docker; basic Kubernetes for deploying AI services
- Monitoring: AI-specific observability — LangSmith, Langfuse, or custom logging for tracing LLM calls in production
Experience
Associate Level (1–2 years)
- 1–2 years of professional experience in AI/ML engineering or software development with a strong AI focus
- Hands-on experience building or integrating LLM-powered applications using OpenAI, Anthropic Claude, Google Gemini, or equivalent
- Practical exposure to conversational AI or chatbot development — prompt engineering, intent handling, or dialogue flow design
- Familiarity with RAG pipeline concepts — document ingestion, embedding, vector search, and retrieval
- Experience with Python and at least one AI orchestration framework (LangChain, LlamaIndex, or similar)
- Exposure to cloud platforms (GCP or Azure) for deploying AI/ML workloads
Mid Level (3–5 years)
- 3–5 years of experience in AI/ML or software engineering, with at least 2 years focused on production-grade LLM or GenAI systems
- Proven experience designing and deploying RAG pipelines in production — including chunking strategies, embedding models, vector databases (Qdrant, Pinecone, Weaviate, or pgvector), and retrieval optimization
- Hands-on experience building conversational AI systems for enterprise clients — chatbot, virtual assistant, or AI agent products in regulated industries
- Demonstrated experience with Voice AI integrations — STT (Deepgram, Whisper, Google Speech-to-Text) and/or TTS (ElevenLabs, Google TTS) in a production environment, ideally with LiveKit Agents SDK or equivalent
- Experience implementing AI evaluation frameworks (RAGAS, DeepEval, or custom eval pipelines) to measure and improve model quality
- Experience with AI observability tooling — LangSmith, Langfuse, or custom LLM call tracing in production
Additional Information
We value a flexible working hour for our employees.
The most important is we provide a learning experience in Conversational AI Industry.