Deep Learning Experts in Berlin

• Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
• Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
• Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
• Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
• Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow

• Developing methods for monocular 3D facial reconstruction and personalized geometric modelling from mobile imagery
• Building learning-based approaches for facial shape estimation, video-based facial analysis, and privacy-preserving visual learning

• Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
• Data and trade architecture development for liquidity provision

• Conducted research and development in the field of large-scale 3D reconstruction

• Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
• Work with customers to understand their challenges and provide the best solutions based on open-source data products
• Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
• Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
• Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
• Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
• Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
• Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible

• Designed and developed image classification models for computer vision tasks, including dataset creation, preprocessing, validation, and visualization; applied machine learning and deep learning techniques to optimize accuracy and performance.
• Conducted experiments with appropriate ML algorithms, LLM-based approaches, and tools; performed statistical analysis, hyperparameter tuning, and fine-tuning using test results for robust model performance.
• Developed and optimized CRUD operation APIs using Node.js (Fastify) and Flask (Python), improving latency and ensuring scalability.
• Implemented backend solutions with SQLAlchemy for database management, and automated workflows using cron jobs and AWS Lambda functions.
• Experienced in implementing and maintaining CI/CD pipelines to streamline deployments and ensure reliable software delivery.
• Consistently achieved service time and quality targets while maintaining strong, collaborative, and professional working relationships across teams.

• Developed YourBestChance.io, an AI-powered career resilience platform that leverages advanced machine learning to provide personalized guidance and resources for users.
• Architected and implemented a Retrieval-Augmented Generation (RAG) system supporting three languages, utilizing GPT-based large language models (including OpenAI and Grok variants) integrated with specialized vector databases for efficient semantic search and similarity matching.
• Built an interactive AI chatbot powered by generative AI and RAG pipelines to deliver real-time, context-aware responses and enhance user engagement.
• Optimized data pipelines and AI infrastructure for scalability, ensuring robust performance under increasing loads and reducing latency by 50%.
• Developed comprehensive AI strategies using ML and Gen AI to create customized growth plans; analyzed company data to identify strengths, weaknesses, risks, and opportunities for AI integration.
• Defined ethical frameworks for AI deployment, assessed workforce and leadership upskilling needs, and built phased action plans (short-, mid-, and long-term) with targeted AI integration recommendations.

• Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
• Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
• Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
• Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.

• Conducting research at the interface of proteomics and artificial intelligence, focusing on the application of machine learning models (e.g., neural networks, clustering algorithms, and feature extraction) to analyze complex biological datasets.
• Developing and teaching AI-based analytical workflows for molecular and proteomic data, integrating tools such as Python (scikit-learn, TensorFlow, Pandas) for predictive modeling and data visualization.
• Collaborating with interdisciplinary teams to explore data-driven hypotheses in molecular genetics and enhance biological interpretation through AI-assisted pattern recognition.
• Implementing automated data processing pipelines to improve reproducibility and FAIR data management in high-throughput experiments.

• Designed and maintained Python- and SQL-based ETL pipelines for multi-source datasets
• Built data validation and monitoring processes to ensure accuracy and reliability
• Delivered structured datasets to support reporting and business decisions
• Collaborated with stakeholders to translate requirements into data solutions

• Designed and implemented a real-time perception pipeline using YOLOv7 on Time-of-Flight (ToF) sensor data, enabling live streaming, inference, and on-frame visualization for passenger detection.
• Fine-tuned and evaluated multiple state-of-the-art monocular depth estimation models for Automatic Passenger Counting (APC), and developed a custom hybrid depth model that improved depth accuracy in challenging scene regions.
• Demonstrated that model-generated depth maps outperform raw sensor depth for APC tasks across several datasets, contributing to measurable reductions in counting error.

• Train and optimize MAVAE/VAE models in PyTorch to detect anomalies in multivariate time-series sensor data.
• Design preprocessing workflows and evaluation pipelines to improve model accuracy and robustness.
• Benchmark MAVAE performance against baseline statistical and deep learning approaches, presenting comparative insights.
• Collaborate with research supervisors to refine hypotheses and translate experimental findings into deployable research outputs.

• Guided systematic data collection and pre-processing for the machine learning algorithms
• Defined the labeling process and implemented an interface to annotate the datasets
• Programmed a visual deep learning algorithm to detect machine pollution in live production
• Implemented data augmentation techniques to deal with machine heterogeneity
• Supplied a containerized model with API endpoints for deployment to the production machines
• Coordinated and represented a five-person project team, prepared presentations and reports

Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.

• Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
• Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
• Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
• Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
• Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.