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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.