About the role
Job Purpose
As an AI Engineer at Tarjama&, you will be responsible for supporting the design, development, and deployment of agentic and multimodal AI solutions leveraging language, vision, and speech technologies. You will contribute to building scalable, production-ready AI products by assisting in model integration, evaluation, optimization, and data pipeline preparation. You will collaborate closely with senior
Requirements
Duties & Responsibilities
AI Agentic Workflows & Frameworks
Design and implement AI agentic workflows that orchestrate language, vision, and speech models for multi-step tasks.
Build and extend task-driven, tool-using AI agents using modern agent frameworks and orchestration patterns.
Implement decision logic, memory strategies (short-term and vector-based), and tool-calling mechanisms in production-ready systems.
Improve agent reliability through structured prompting, planning strategies, and basic error-handling mechanisms.
Multimodal AI Development (Text, Vision, Speech)
- Develop and integrate text-based AI systems, including LLM pipelines, embeddings, rerankers, and RAG architectures. - Build document understanding pipelines using OCR and vision-based models to extract structured insights from complex documents. - Develop and optimize speech-based AI solutions, including STT, TTS, and conversational voice interfaces. - Design and improve multilingual and translation pipelines with attention to performance and quality. - AI Product Development & Deployment - Own the development of specific AI components or features from design through deployment. - Deploy, monitor, and optimize AI systems in production with guidance on scalability and cost-efficiency. - Collaborate with engineering teams to integrate AI components into maintainable and scalable systems. - Implement logging, monitoring, and basic observability practices to track system performance. - Model Evaluation & Optimization - Define and apply evaluation metrics for AI systems, including accuracy, latency, and robustness. - Benchmark and optimize models for inference performance and cost-efficiency. - Run experiments and A/B tests to validate improvements and support data-driven decisions. - Identify common failure modes and contribute to improving model robustness and reliability.
Data Processing & Pipeline Support
- Design and maintain data pipelines for text, document, and audio data across training and inference workflows. - Ensure data quality and consistency in collaboration with data teams. - Analyze model outputs and user interactions to support iterative system improvements.
Cross-Functional Collaboration
- Collaborate with product managers, designers, and engineers to translate requirements into scalable AI solutions - Partner with senior engineers on system design while independently handling well-scoped components. - Support integration, testing, and deployment of AI systems in production environments.
Documentation & Knowledge Sharing
- Maintain clear documentation for AI pipelines, agent workflows, and evaluation processes. - Contribute to team knowledge sharing through code reviews, demos, and technical discussions. - Follow and help reinforce best practices for AI development and deployment.
Text as published by Arabic.AI. We don't edit postings; apply and ask questions on the employer's site.