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.