AI Engineer
Kata.ai · South Quarter Building, Tower C, Level 10, Jl. R.A. Kartini Kav.8, South Jakarta, DKI Jakarta, Indonesia
Sekilas tentang pekerjaan ini
AI Engineer di Kata.ai merancang, membangun, dan menerapkan sistem AI untuk klien enterprise di sektor pemerintahan, jasa keuangan, kesehatan, dan telekomunikasi. Posisi ini berlokasi di South Jakarta; tingkat pengalaman tidak disebutkan.
Tugas utama
- Merancang dan membangun sistem AI siap produksi
- Menerapkan agen percakapan berbasis LLM dan pipeline RAG
- Mengembangkan alur kerja NLP dan integrasi voice AI
- Menerapkan solusi AI bagi klien enterprise di berbagai sektor
Kualifikasi utama
- Gelar sarjana di bidang ilmu komputer, AI, data science, atau bidang terkait
- Menguasai Python; FastAPI untuk layanan AI
- Memahami integrasi LLM seperti OpenAI GPT-4o, Anthropic Claude, atau Google Gemini
- Menguasai framework seperti LangChain, LlamaIndex, atau CrewAI
- Memahami pipeline RAG, vector database, dan evaluasi AI
Ringkasan ini dibuat dengan AI dari deskripsi di bawah. Yang berlaku adalah deskripsi lengkapnya di situs Kata.ai.
Deskripsi pekerjaan
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 & 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