As an AI Engineer, you will build scalable AI solutions that improve user experiences, automate operations, and address real business needs. Our work spans Agentic AI, intelligent search, speech processing, and foundation-model-based applications. You will take ownership of AI projects end-to-end, from research and prototyping to evaluation, deployment, and continuous improvement.
 

• Collaborate with product, engineering, and operations teams to translate business needs into well-defined technical problems, measurable success criteria, and actionable implementation plans.
• Design, build, and deploy LLM-powered applications and AI agents, including conversational workflows, tool and API integrations, retrieval mechanisms, state management, and error recovery.
• Select, evaluate, and adapt foundation models using appropriate techniques such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning.
• Contribute to semantic and hybrid search, ranking, reranking, and speech-related initiatives based on team and business priorities.
• Design evaluation datasets and reproducible experiments to measure model quality and end-to-end system performance.
• Investigate system failures, analyze error patterns, and identify opportunities for improvement across data, models, prompts, tools, and system architecture.
• Write clean, maintainable, production-quality code and contribute to deployment, monitoring, troubleshooting, and ongoing system maintenance.
• Optimize model quality, latency, inference costs, and resource utilization within product and infrastructure constraints.
• Document technical decisions, participate in code reviews, and share knowledge and best practices across the team.
• Implement appropriate safeguards for data privacy, access control, and AI agent actions.

• Bachelor’s degree or higher in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical field.
• At least 3 years of relevant professional experience in AI engineering, machine learning, or software engineering focused on AI-powered products.
• Hands-on experience building and deploying at least one machine learning or foundation-model-based solution in a real-world environment.
• Strong Python programming skills and solid software engineering fundamentals, including modular design, version control, testing, debugging, and API integration.
• Good understanding of machine learning and deep learning fundamentals, including Transformers, embeddings, and model evaluation.
• Practical experience working with large language models (LLMs) and a solid understanding of RAG, tool calling, and agentic workflows.
• Ability to build evaluation datasets, analyze errors, and distinguish between data-related, model-related, and system-level issues.
• Experience with a major deep learning framework, such as PyTorch, and tools for working with pretrained models.
• Familiarity with Linux, Docker, and the fundamentals of service deployment and monitoring.
• Ability to read technical documentation and research papers in English and apply relevant findings to practical engineering problems.
• Strong problem-solving, analytical thinking, independent learning, communication, and collaboration skills.
• A strong sense of ownership and the ability to navigate ambiguity, make informed technical decisions, and deliver results.
  Experience in one or more of the following areas is a plus:
• Building production-ready AI agents with multiple tools, state management, and robust failure recovery.
• Working with open-source or open-weight models, fine-tuning, model serving, and inference optimization.
• Developing search, retrieval, ranking, reranking, or recommendation systems.
• Working with automatic speech recognition (ASR), text-to-speech (TTS), audio processing, or real-time conversational AI systems.
• Working with Persian-language data and addressing language-specific modelling and evaluation challenges.
• Building high-traffic services, asynchronous processing pipelines, or systems running on GPU infrastructure.