Computer Vision Experts in Berlin

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices

• 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

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

• 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

• 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

• Design of an LLM-based agent system to control the ERP software
• Development of agent workflows with LangGraph and PydanticAI
• Planning interfaces between business logic and language models
• Planning agent orchestration
• Prototype development and demonstration

Tools: Python, Pydantic, React, LangChain, LangGraph, Linux

• 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.

• 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.

• 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

• Lead a team of 15 annotators on large-scale computer vision projects for autonomous vehicle systems
• Developed comprehensive annotation guidelines that improved inter-annotator agreement by 35 percent
• Implemented quality control processes that reduced error rates by 42% across all projects
• Collaborated with ML engineers to identify edge cases and improve dataset quality
• Managed annotation projects for Fortune 500 clients, delivering 100% on time

• Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
• Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
• Identified performance gaps and improved tool adoption by 65%.
• Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
• Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.

• Contributed to the Karakutu project, developing AI-driven tools to analyze news in Turkey.
• Assisted in web scraping, applied NER for entity extraction, and built interactive filtering interfaces (Vue.js, Plotly.js) for entity and location based search.
• Performed sentiment and content-shift analysis to detect editorial influence in modified news articles.

• 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.

• Built a hybrid semantic and keyword search and LLM-based requirement extraction from conversational queries, boosting search accuracy by 85%, cutting zero-result searches by 70%, and reducing search time by 60%.
• Deployed a production-ready API with monitoring dashboards over 100K+ products, keeping response times under 2s and reducing customer search-to-purchase time by 40%.
• Technologies: Python, BGE-M3, Qwen2.5, FastAPI, Qdrant, Meilisearch, Docker, Prometheus, vLLM.