MLflow Experts in Berlin
in minutes from over 15,000 CVs with the power of AI.Hire experts who set up MLflow tracking, model registry, experiment management, and deployment workflows for production teams. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used MLflow
Abhishek Nair
Last position:
Fullstack Developer at DAMALO GmbH
- Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
- Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
- Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
- Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
- Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
- Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
- Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.
Haseeb Zahid
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Sejal Vaidya
Last position:
Data & ML Engineering at Consulting
- 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
Wolfram Knan
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- 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
Muzamal Ali
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Hamza Khan
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- 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.
Mathias Wilhelm
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Louis Guitton
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- 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
Ashwin Parthasarathy
Last position:
Data Scientist at Mercor Intelligence
- 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.
Julien Look
Last position:
MLOps Engineer at SAMGEN
- 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
Tobias Jaeuthe
Last position:
Design of an AI-Agent-Based ERP System
- 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
Tushar Rao
Last position:
Research Assistant/Master Thesis at Otto-von-Guericke Universität Magdeburg
- Performed qualitative and quantitative analysis of extracted findings, categorizing themes, evaluating methodologies, and assessing study quality and reliability.
- Produced research reports and evidence summaries communicating key trends, gaps, and opportunities to academic advisors or cross-functional teams.
- Presented findings through well-structured visualizations, tables, and narrative summaries to support decision-making and guide future research directions.
Fares Kallel
Last position:
Research Assistant – AI & Computer Vision at Iris-Sensing GmbH
- 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.
Kashaf Khan
Last position:
AI Consultant / Expert at Siemens Mobility
- 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.
Umar Maqsud
Last position:
Senior AI Architect & Engineer at Freelancer / Self-employed
- Consulting, design, and architecture of SaaS platforms with a focus on automation, data analytics, and cloud deployment
- Defining the target architecture and managing the entire development lifecycle from implementation to production operation, including stakeholder alignment
- Designing, architecting, and implementing a multi-tenant SaaS platform
- Building scalable data and machine learning pipelines (batch & streaming) for order and business data
- Developing AI models for data analysis (KPI calculations, forecasts) and integrating them into data pipelines
- AI-driven processing of customer inquiries (delivery status, invoices, cancellations, complaints) to automate customer service
- Developing APIs, microservices, and dashboards with Python for data-driven applications
- Cloud deployment on AWS and infrastructure-as-code automation with Terraform; containerization with Docker and Kubernetes
- Setting up CI/CD pipelines for automated deployments with GitLab CI and governance of deployment processes
- Implementing monitoring dashboards with Grafana to monitor services and ML pipelines
- Implementing security and compliance requirements (GDPR-compliant data handling, logging), including identity & access management and role-based access control
Discover over 15,000 top freelancers
Statistics of experts using MLflow
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
1.6 years (Germany: 1.7 years)
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Healthcare, Education
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
67% (Germany: 80%)
Doctorate
11% (Germany: 19%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
English, German, French
Speak two or more languages
89% (Germany: 96%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Berlin using MLflow
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Experiment tracking
MLflow helps teams record model runs, parameters, metrics, and artifacts in one place. That makes it easier to compare training results, reproduce work, and keep a clear audit trail across data science projects. It is a practical fit for companies that need order around fast-moving model work.
Core parts
MLflow is usually used through a few connected pieces:
- Tracking for runs, metrics, and artifacts
- Projects for reproducible execution
- Models for packaging and deployment
- Model Registry for versioned approvals
Strong specialists know how these parts fit into a wider MLOps setup.
Where it fits
Teams bring in MLflow expertise when models move from notebooks into repeatable workflows. It is common in forecasting, recommendation systems, fraud detection, and NLP pipelines. In Berlin, it often supports product teams, data-driven startups, and larger companies that want a cleaner path from experiment to release.
What strong experts do
Good MLflow professionals do more than install the tool. They design logging standards, organize model versions, connect storage and compute, and make results easy to review. They also work with Python, Docker, cloud services, and the surrounding ML stack so tracking stays useful after the first prototype.
When to hire
Bring in freelance support when your team needs to structure experiments, clean up a messy registry, or connect MLflow to an existing platform. It also helps when releases are blocked by unclear model lineage or weak reproducibility. The best experts can join remotely or on-site in Berlin, depending on your team’s workflow.
Quality signals
Look for professionals who can explain how they handle naming, tags, artifacts, approvals, and rollback paths. They should be comfortable with Databricks MLflow as well as open-source MLflow, and they should understand the difference between tracking a model and operating it safely. Clear documentation and consistent run history are strong signs of quality.
Frequently asked questions
The facts hiring teams ask for most often when it comes to MLflow.
MLflow is used to track experiments, package models, and manage model versions across a machine learning workflow. Companies use it to make training results reproducible and easier to review before release. It is especially useful when several specialists work on the same model over time.
MLflow adds structure that notebooks alone do not provide. Instead of losing track of runs, metrics, and artifacts in scattered files, teams get a consistent record of what changed and why. That makes handovers, reviews, and deployment much cleaner.
MLflow is often a good choice when a team wants flexibility and control over the workflow. Managed MLOps tools may be easier to start with, but MLflow can fit better when you need a lighter stack or want to stay close to existing cloud and data tools. The right choice depends on how much control you need over tracking, registry, and deployment.
A strong MLflow specialist usually also knows Python, model packaging, Docker, and basic cloud setup. They should understand experiment design, artifact storage, and how model versions move through review and release steps. Familiarity with Databricks and the wider MLOps toolchain is often useful too.
A small MLflow setup can be handled by a specialist who has done similar tracking and registry work before. More complex work needs someone who understands production workflows, approvals, and integration with existing data platforms. The more systems that must connect, the more important hands-on experience becomes.
Yes, MLflow works well with remote collaboration because experiment tracking and model review are easy to share across locations. For Berlin teams, freelancers often join remotely for setup and documentation, then come on-site only when workshop sessions or team alignment are needed. That keeps the work flexible without losing clarity.
A good MLflow freelancer leaves you with clean run naming, useful tags, reliable artifact storage, and a registry flow your team can actually maintain. Ask for examples of how they handled reproducibility, versioning, and release checks. Clear documentation and simple handover notes are strong signs they understand production work.
MLflow started as an open-source project and is also used within the Databricks ecosystem. In practice, people often say Databricks MLflow when they mean the managed environment around it. A good specialist should understand both the open-source tool and how it behaves in Databricks setups.
The average hourly rate of freelancers in Berlin, Germany who have used MLflow in their recent projects is 81 €, which corresponds to a daily rate of about 644 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used MLflow in their recent projects, 100% hold at least a Bachelor's degree, 67% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Berlin, Germany who have used MLflow in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Berlin, Germany who have used MLflow in their recent projects are English (100%), German (89%), and French (28%).
The most common industries among freelancers in Berlin, Germany who have used MLflow in their recent projects are Information Technology (94%), Healthcare (56%), and Education (50%).
The most common business areas among freelancers in Berlin, Germany who have used MLflow in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (94%).
Main locations of FRATCH Experts, who have recently used MLflow
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
