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.