AI Engineer

شرح موقعیت

· Hands-on experience building and deploying Machine Learning or AI solutions in a production environment. · Strong understanding of machine learning fundamentals, including model selection, training, evaluation, experimentation, data preparation, generalization, and error analysis. · Strong problem-solving ability: able to take an ambiguous business or product problem, formulate it as an AI/ML problem, and determine an effective solution. · Experience building end-to-end AI systems, from understanding the problem and data through experimentation, evaluation, deployment, and iteration. · Familiarity with multiple approaches to AI/ML, including classical machine learning, deep learning, foundation models, embeddings, retrieval, prompting, fine-tuning, and agentic systems. · Ability to select and combine different techniques based on the problem rather than being tied to a particular model, framework, or methodology. · Strong familiarity with modern AI development practices, including AI-assisted development, coding agents, rapid experimentation, and automated evaluation. · Ability to use modern AI development tools effectively to research, prototype, implement, debug, evaluate, and iterate on AI solutions while maintaining technical ownership of the resulting system. · Strong understanding of AI system evaluation, including selecting meaningful metrics, designing experiments, analyzing failure cases, and measuring real-world impact. · Solid Python skills sufficient to develop, understand, test, and productionize AI/ML systems. · Proficiency in SQL and experience working with real-world data. Duties · AI Problem Solving: Identify, formulate, and solve complex business and product problems using AI/ML. · AI System Design: Design end-to-end AI systems by combining models, data, prompts, retrieval, tools, agents, evaluation, and traditional software components where appropriate. · Approach Selection: Investigate different solution strategies and determine the appropriate trade-offs between traditional ML, deep learning, foundation models, fine-tuning, RAG, prompting, and agentic approaches. · Rapid Experimentation: Build baselines, prototype alternative approaches, run experiments, analyze failures, and rapidly iterate toward effective solutions. · AI-Native Development: Use modern AI development tools and coding agents to accelerate research, implementation, experimentation, debugging, testing, and iteration. · Model Development: Develop, train, fine-tune, or integrate ML models when the problem requires it. · AI Application Development: Build practical systems around foundation models, including LLM applications, retrieval systems, structured generation, tool use, and agents. · Evaluation: Design and maintain evaluation processes that measure AI system quality, identify failure modes, and guide further development. · Productionization: Turn successful solutions into reliable, scalable, observable, and maintainable production systems in collaboration with engineering teams. · Continuous Improvement: Use production feedback, experiments, and evaluation results to continuously improve AI systems. Preferred Competencies · Experience building LLM applications, RAG systems, agentic systems, or other foundation-model-based applications. · Experience with model fine-tuning or parameter-efficient fine-tuning. · Experience building automated evaluation and benchmarking systems. · Familiarity with AI coding agents and AI-native development workflows. · Experience with MLflow or similar experiment-tracking and MLOps tools. · Experience in one or more applied AI domains such as search, recommendation, NLP, computer vision, forecasting, or ranking. · Familiarity with software engineering practices such as Git, testing, debugging, and CI/CD. · Demonstrated ability to quickly learn and apply new AI technologies, models, and development methodologies.