MLOps Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used MLOps
Deepak Mishra
Last position:
Lead ML Platform Engineer at Billie GmbH
- 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
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
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
Unnikuttan Velamkudy Vijayan
Last position:
Managing Director (Co-Founder) at AathmaSignals
- Spearheading investor outreach and partnership development as founding MD, building the business case and technical narrative needed to attract initial funding and strategic collaborators in the digital health space
- Designing multi-agent AI systems for autonomous biosignal analysis, orchestrating LLM-based reasoning pipelines with domain-specific medical context to enable intelligent, clinical decision support
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
Jeet Pattanaik
Last position:
Global SAP Program Manager at Aldi Sued
- Pioneered first enterprise AI-SAP integration at ALDI SÃœD, deploying AI-driven automation within one of retail's largest SAP S/4HANA programs, eliminating 50% of manual pre-cycle validation time and establishing replicable automation framework across 11 countries
- Led end-to-end SAP project lifecycle management for implementations across SAP S/4HANA and Manhattan Systems, supporting 7,300+ ALDI SÃœD locations globally across Europe and Australia
- Served as primary executive liaison to C-level stakeholders across 11 countries for strategic SAP transformation programs
- Orchestrated automation, performance, and volume testing for critical releases, maintaining 99.9% system SLA compliance during peak retail periods
- Managed cross-functional international teams of 15+ specialists, delivering projects 20% faster than industry benchmarks
- Standardized SAP processes across 11 countries as part of one of retail's largest SAP implementations
- Directly managed €2M budget with 98% allocation accuracy across 12 concurrent projects
- Reduced SAP S/4HANA migration costs by 18% through strategic vendor contract renegotiations and optimization
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
Tino Truppel
Last position:
Director Technology at Forte Digital Germany
- Leading 20+ staff in development, site reliability engineering, and architecture.
- Leading the group-wide agentic AI initiative (Norway, Poland, Germany).
- Hands-on solution architect and AI consultant for over 50% of my working time on client projects in the publishing sector – from local publishers to international corporations.
- Strategic consulting and technical implementation of AI workflow platforms (n8n, Workato).
- Developing prototypes for traditional, AI-based, and agentic AI workflows.
Stefan Ojanen
Last position:
AI Consultant & Advisor at Freelance
- Consulting and advisory services for the AI space on product management, strategy, AI models, AI infrastructure
- AI Product Lead for Ringier AG:
- BliKI chatbot for Blick.ch - live, tens of thousands of users
- AI Forge journalist tooling for Blick.ch - live, hundreds of internal users
- Floorian automated ad floor price optimization system - pilot ongoing
- Freelance CTO for an AI-as-a-Service company focusing on automated trading solutions
- AI Agent calls people on the phone at scale, converses to achieve specific goals, and takes action based on how the conversation
- AI Trading bot based on transformer time-series model forecasting future asset prices
Daniel Christoph
Last position:
AI Engineer at EMLI GmbH
- Development and deployment of AI/ML models to support research, production, and QC processes in GxP-regulated life science environments
- Building scalable MLOps infrastructures for the production use of AI solutions in regulated areas, including cloud architectures and data pipelines
- Regulatory compliance and validation according to GAMP 5, EU AI Act, and data integrity requirements, including audit trail-compliant documentation
- Interdisciplinary project management in AI and digitalization projects: coordinating stakeholders, budget responsibility, client communication
- Data engineering and integration: analyzing diverse production data, ensuring data quality, and integration into validated systems
Gyan Prakash
Last position:
Senior DevOps and Cloud Architect at Bosch
- Architected and operated cloud-based data and ML platforms for autonomous driving and parking systems, supporting large-scale (multi PB scale) simulation and vehicle data ingestion.
- Implemented security, compliance, and governance standards across Azure subscriptions and cloud resources.
- Managed GitHub organizations and CI/CD pipelines to improve deployment reliability and developer productivity.
- Contributed to hiring and technical interviews as part of the recruitment panel.
Discover over 15,000 top freelancers
Statistics of experts using MLOps
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 13 years)
Position duration
2.2 years (Germany: 2.9 years)
Positions per freelancer
7 (Germany: 9)
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Healthcare, Professional Services
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
68% (Germany: 78%)
Doctorate
12% (Germany: 23%)
Certifications per freelancer
2 (Germany: 3)
Most common languages
English, German, French
Speak two or more languages
92% (Germany: 97%)
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 MLOps
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
Production ML
MLOps connects machine learning work with reliable delivery. It covers the steps from training and testing models to deployment, monitoring, and retraining. Companies use it to move ML from notebooks into systems that stay stable in real use.
Core stack
- Model versioning and experiment tracking
- CI/CD for training and deployment
- Feature stores and data validation
- Monitoring for drift, latency, and quality
- Reproducible pipelines in cloud or Kubernetes
Strong professionals know the full path, not just one tool. They understand data flow, environment parity, and how to keep training and serving aligned.
When to bring help
Teams often need freelance support when a model is ready for production but the delivery process is not. The same is true when retraining, approvals, or observability become hard to manage. In Berlin, this comes up in startups, scale-ups, and enterprise teams working in regulated or data-heavy environments.
What good work looks like
A strong MLOps specialist writes clean pipeline logic, sets clear checks, and documents handoffs. They know how to reduce manual steps without hiding failures. They also work well with data specialists, platform specialists, and product teams.
Common projects
- Building training and deployment pipelines
- Setting up model monitoring and alerts
- Adding automated tests for data and models
- Introducing rollout, rollback, and approval steps
- Improving reproducibility across environments
This work often involves Python, Docker, Kubernetes, GitHub Actions, MLflow, Kubeflow, Airflow, and cloud services. The exact stack depends on how much control a team wants over training, serving, and governance.
Berlin collaboration
Berlin teams often want specialists who can work remote first and still join on-site sessions when needed. Good communication matters because MLOps touches data, infrastructure, and model ownership at the same time. Clear documentation and practical handover are essential.
Frequently asked questions
Not sure where to start with MLOps? These answers cover the essentials.
A strong MLOps specialist helps build the path from model training to production use. That usually includes pipelines, deployment steps, monitoring, and retraining flows. The goal is to make machine learning repeatable, traceable, and safe to run.
MLOps focuses on operating models in real systems, while classic machine learning work often stops at experimentation and model quality. It adds release discipline, observability, and environment control. If a model matters to the business, MLOps closes the gap between research and delivery.
A good MLOps freelancer usually works with Python, Docker, Kubernetes, and one or more pipeline tools such as Airflow or Kubeflow. Model tracking tools like MLflow also come up often, along with cloud platforms and Git-based delivery. Data validation and monitoring skills are just as important as tooling.
Companies usually bring in MLOps support when models are ready for production but the delivery process is fragile or manual. That also happens when monitoring is missing, retraining is unclear, or teams need better release controls. External help is useful for setting a clean baseline fast.
MLOps work can be done fully remote in many cases, especially when the specialist can access cloud tools and collaborate clearly. In Berlin, some teams still prefer on-site sessions for security reviews, architecture decisions, or kickoff workshops. A hybrid setup often works best for complex production systems.
Look for someone who has shipped MLOps workflows end to end, not just set up one tool. They should explain how they handle testing, deployment, rollback, monitoring, and retraining. Strong specialists also document their work well and can work across data and platform teams.
No, MLOps is useful for teams of many sizes. Smaller teams need it to avoid fragile manual processes, while larger teams need it to keep multiple models manageable. The right setup depends on risk, release pace, and how many models are in production.
A strong MLOps specialist often works alongside data engineering, cloud infrastructure, and software delivery expertise. Knowledge of security, governance, and monitoring also helps a lot. For teams in Berlin, solid English communication is usually enough, though German can help in workshops and stakeholder reviews.
The average hourly rate of freelancers in Berlin, Germany who have used MLOps in their recent projects is 95 €, which corresponds to a daily rate of about 758 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used MLOps in their recent projects, 100% hold at least a Bachelor's degree, 68% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers in Berlin, Germany who have used MLOps in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers in Berlin, Germany who have used MLOps in their recent projects are English (100%), German (92%), and French (15%).
The most common industries among freelancers in Berlin, Germany who have used MLOps in their recent projects are Information Technology (96%), Healthcare (38%), and Professional Services (38%).
The most common business areas among freelancers in Berlin, Germany who have used MLOps in their recent projects are Information Technology (100%), Product Development (88%), and Business Intelligence (69%).
Main locations of FRATCH Experts, who have recently used MLOps
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.
Hamburg 
Munich 
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Philipp Thomaschewski
FRATCH CEO
