Amazon SageMaker 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

• Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
• Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
• Exploring Agentic AI & LLM-based tooling for production readiness patterns

• 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.

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

• Assessment of the existing reporting landscape and strategic bundling of needs
• Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
• Building and maintaining data pipelines

Stack: Metabase · ClickHouse · Appsmith · Airflow

• Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
• Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
• Orchestrated pipelines with Azure Data Factory
• Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
• Led the Data Engineering team (3 members) in a functional role
• Conducted workshops to optimize and stabilize the data platform and the development process
• Collected and prioritized new requests, maintained the product backlog
• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural

• Elevated LLM output reliability by engineering domain-specific prompts and evaluation logic, improving reasoning consistency across production language model workflows.
• Designed advanced coding benchmarks and validated solutions to strengthen training and evaluation datasets, improving model performance on technical problem-solving tasks.
• Designed and implemented automated evaluation frameworks for technical reasoning tasks; optimized LLM output reliability by 15% through rigorous prompt engineering and rubric-based benchmarking.

• Building and scaling cloud infrastructure on GCP to support a SaaS platform for industrial clients
• Designing and implementing a data-driven DevOps pipeline for streamlined deployment and CI/CD workflows
• Collaborating with Data Science team on MLOps workflow to automate integrated retraining

• Led the end-to-end delivery of a SAP Supply Chain Management (SCM / TD / SD) ERP programme, covering project initiation, detailed requirements gathering, operating model definition, system design, build, testing, cutover, and global Go Live across Europe, Asia, and North America. Ensured the ERP solution supported key supply chain, manufacturing, and planning operations to enable future business growth.

• Conducted cross-functional workshops with Supply Chain, Procurement, Planning, Manufacturing, and Logistics teams to capture business requirements, define the future operating model, and map end-to-end system design. Consolidated over 150 requirements into structured documentation aligned with SAP standards.

• Shaped solution design and vendor engagement during the early discovery phase, supporting selection of best-fit technology partners and ensuring the system design covered production planning, inventory management, warehousing, logistics, and supply chain forecasting.

• Managed D365 configuration and troubleshooting, ensuring alignment with business processes and resolving integration issues between D365, SAP SCM modules, and surrounding systems.

• Supported Grain data model changes to lead ingestion of planning data into Snowflake and Footprint, enabling enterprise reporting and analytics development.

• Managed scope, timelines, risks, and dependencies across international teams spanning Europe, Asia, and the US, maintaining integrated project plans, issue logs, and executive reporting to drive stakeholder alignment and delivery momentum.

• Enabled the integration of AI-powered demand forecasting tools into supply chain planning processes, improving forecast accuracy, inventory turnover, and operational decision-making across multiple regions.

• Led SIT, UAT, and data migration phases, including design of test scenarios, defect triage management, and coordination of test execution to validate supply chain and manufacturing workflows prior to deployment.

• Delivered detailed cutover planning, business readiness activities, and hypercare support, ensuring a smooth and coordinated Go Live and full operational handover to business 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.

• Supported partner centers in adopting AI and LLMs-based predictive models for small-molecule discovery and therapeutic response using their genomic databases, designing failure modes for AI, and reducing feature-engineering time by 25%.
• Performed Python analysis and improved domain motion coverage by 30% through integration of free energy data with conformational modeling, mapping heterogeneous protein states for accurate structure–function analysis.

• 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