Job Purpose  As a Senior AI Engineer at Tarjama&, you will be responsible for designing, building, and deploying advanced AI systems that power language, document, and speech intelligence across our products.
You will translate complex business and product needs into scalable, high-quality AI solutions, ensure their reliability in production, and continuously optimize their performance, accuracy, and cost-efficiency.
  You will play a key role in shaping Tarjama&’s AI architecture and best practices, collaborating closely with product, engineering, and data teams to deliver impactful, real-world AI applications that enhance our localization, content, and technology solutions.
  Duties & Responsibilities  Multimodal AI Development  Develop and integrate text-based AI systems, including LLM pipelines, embeddings, rerankers, and scalable RAG architectures.
  Build and optimize document understanding systems using OCR, layout-aware vision models, and multimodal reasoning.
  Develop speech-based AI solutions, including STT, TTS, and conversational voice agents.
  Design and deploy multilingual and translation pipelines, ensuring quality, latency, and scalability across languages.
  AI Product Development & Deployment  Own the development of AI-powered features from concept through production, aligning solutions with product and business goals.
  Deploy, monitor, and optimize AI systems in production environments, ensuring reliability, scalability, and cost-efficiency.
  Collaborate closely with software engineering teams to integrate AI components into secure, maintainable, and scalable architectures.
  Implement observability, logging, and monitoring to support continuous improvement of AI systems.
  Model Evaluation & Optimization  Define evaluation strategies and metrics for multimodal AI systems, including accuracy, latency, robustness, and user impact.
  Benchmark, fine-tune, and optimize models for inference performance, cost-efficiency, and scalability.
  Conduct experimentation and A/B testing to validate model and system improvements.
  Identify and mitigate model failure modes, biases, and performance regressions.
  AI Agentic Workflows & Frameworks  Design, implement, and maintain AI agentic workflows that orchestrate language, vision, and speech models to solve multi-step, real-world tasks.
  Build and extend task-driven, tool-using AI agents using modern agent frameworks and orchestration patterns.
  Implement decision logic, memory strategies (short-term, long-term, vector-based), and tool-calling mechanisms for production-grade AI systems.
  Improve agent reliability through structured prompting, planning strategies, and error-handling mechanisms.
  Data Processing & Pipeline Management  Design, implement, and maintain data pipelines for text, document, and audio data used in training, evaluation, and inference.
  Ensure data quality, governance, and security in collaboration with data and platform teams.
  Analyze model outputs and user interactions to drive iterative improvements in system performance.
  Cross-Functional Collaboration  Work with product managers, designers, and engineers to translate requirements into robust AI solutions.
  Partner with senior engineers on system design decisions and technical direction.
  Provide technical guidance during integration, testing, and production rollout phases.
  Documentation & Knowledge Sharing  Maintain comprehensive documentation for AI architectures, agent designs, prompts, and evaluation frameworks.
  Share knowledge and best practices with peers through reviews, demos, and internal documentation.
  Contribute to improving team standards for AI development and deployment.
Education, Experience & Qualifications  Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field.
  3-5 years of hands-on experience building and deploying production-grade AI/ML systems, with a strong focus on LLMs, NLP, and multimodal AI (text, vision, and/or speech).
   Proven experience developing AI agentic systems using LLMs, including task orchestration, tool/function calling, planning strategies, and short- and long-term memory (e.
g., vector stores).
  Strong proficiency in Python, with practical experience using PyTorch and/or TensorFlow for model development, fine-tuning, and optimization.
   Demonstrated experience designing and scaling RAG architectures, search pipelines, embeddings, rerankers, and multimodal applications.
   Hands-on experience with document understanding systems, including OCR, layout-aware models, and vision-language reasoning, is highly desirable.
   Experience building or integrating speech-based AI systems (STT, TTS, conversational voice agents) and/or multilingual and translation pipelines is a strong plus.
  Solid understanding of model evaluation and optimization, including defining metrics, benchmarking, latency/cost optimization, A/B testing, and identifying failure modes or bias.
  Experience deploying AI systems using APIs, microservices, and cloud-based infrastructures, with familiarity in observability, logging, and monitoring best practices.
  Working knowledge of prompt engineering, fine-tuning strategies, and inference optimization for reliable, production-grade AI systems.
  Strong analytical and problem-solving skills, with a product-oriented mindset and passion for applying AI to real-world problems.
  Experience applying AI in regulated or domain-specific industries (e.
g., legal, finance, enterprise) is a plus.
  Fluency in both English and Arabic is essential, with the ability to work in bilingual product and technical contexts.
  Behavioral Competencies  Adaptability  Problem Solving  Initiative  Team Oriented  Ability to work under pressure  Technical Competencies  LLM Development & Fine-Tuning  Natural Language Processing (NLP) & Generative AI  AI Agent & Workflow Automation  Machine Learning Engineering