AI Engineer
Core
Design, develop, and deploy agentic and multimodal AI solutions leveraging language, vision, and speech technologies to build scalable, production-ready AI products.
Role type
AI Engineer (Agentic Workflows & Multimodal Systems)
Builds
Agentic workflows, multimodal AI systems (text, vision, speech), and data pipelines for training and inference.
Domain
Artificial Intelligence, Machine Learning, Multimodal Systems
Deliverable
production ML models
Required skills
AI agent orchestration, LLM pipeline development, RAG architectures, OCR and vision-based document understanding, speech-to-text (STT) and text-to-speech (TTS) development, multilingual translation pipelines, model evaluation and optimization, data pipeline design, system monitoring and observability, A/B testing, error handling and planning strategies.
Preferred skills
Experience with modern agent frameworks, vector-based memory strategies, tool-calling mechanisms, cost-efficiency optimization, cross-functional collaboration on system design.
Technologies
LLMs, embeddings, rerankers, OCR, STT, TTS, vector databases, agent frameworks, data pipelines.
Responsibilities
Design and implement AI agentic workflows that orchestrate language, vision, and speech models; develop and integrate text-based AI systems including LLM pipelines and RAG architectures; build document understanding pipelines using OCR and vision-based models; develop and optimize speech-based AI solutions including STT, TTS, and conversational voice interfaces; own the development of specific AI components from design through deployment; define and apply evaluation metrics for AI systems including accuracy, latency, and robustness; design and maintain data pipelines for text, document, and audio data across training and inference workflows.
Seniority
Mid-level, hands-on IC