Mohammad Akbari Monfared

Hamburg, DE

• Led the full AI development lifecycle — design, implementation, project management, and stakeholder alignment with non-technical founders and business teams.
• Architected and shipped AIME, an Agentic RAG ecosystem at aimetalent.com, cutting a multi-day manual recruitment process to minutes by automating global sourcing and matching.
• Engineered a custom Agentic CV Matcher using RAG for high-precision candidate ranking via deep semantic search across massive databases.
• Built autonomous AI pipelines that ingest CVs and extract standardized summaries and key skills, ensuring consistent, searchable profiles at scale.
• Designed and led a custom recruitment CRM, managing two developers and automating invoicing, margin calculation, and document generation.
• Owned end-to-end cloud and data architecture on AWS, with S3, MongoDB, and RDS plus external service and job-board integrations.

Mönchengladbach, DE

• Developed Python workflows to process high-frequency sensor data (10GB per 10-minute window), extracting compact summaries that solved storage bottlenecks while preserving critical signal.
• Formulated the optimization problem mathematically and designed a custom statistical model for forecasting and anomaly detection that outperformed standard models by 30%.
• Deployed the solution end-to-end with factory systems via gRPC and SQL, delivering real-time dashboards and increasing factory efficiency by 2%.
• Partnered with domain experts and senior management to translate operational challenges into ML solutions, shaping the technical roadmap and retraining logic.

Bonn, DE

• Developed the first agentic framework for Aspect-Based Sentiment Analysis (ABSA), using an LLM "Generator-Evaluator" (LLM-as-judge) workflow to produce label-consistent synthetic data; published at EACL 2026.
• Closed the performance gap for lightweight models with self-reflection logic, yielding a 6% F1-score improvement over standard augmentation.
• Designed a systematic evaluation framework across four SemEval benchmarks, proving agentic augmentation can match real-world data performance in low-resource scenarios.
• Engineered the end-to-end Python pipeline studying task complexity and data ratios on downstream ML performance for data-scarce training strategies.

London, UK

• Developed and maintained Python scripts automating internal data reporting workflows, saving the operations team several hours per week with repeatable pipelines.
• Built and consumed RESTful API endpoints, working across request/response cycles, authentication flows, and backend service integration.
• Queried and maintained SQL databases as part of feature delivery, developing strong proficiency in structured data systems and schema design.
• Contributed to a shared codebase using Git, following code review processes and resolving merge conflicts independently.