Reliability Engineer Maintenance, MTTR, Failure Analysis, Statistics, SAP PM, Remote
Llamazing Digital Engineering GmbH
Ansprechpartner: Björn Froese
BerlinBrandenburg, Deutschland
Endkundenprojekt
ITSix SigmaComputerized Maintenance Management SystemFmeaR (Programmiersprache)Ibm MaximoPythonInstandhaltungReliability Centered MaintenanceSAP PMWahrscheinlichkeit und StatistikMttrBeschreibung
For agentic AI projects in industrial manufacturing in the DACH region, we are looking for experienced reliability engineers from maintenance (m/f/d) to join our expert pool. The work is 100% remote, and we welcome applications from anywhere in the world.
A typical project looks like this:
Starting point
A manufacturing company wants to handle production disruptions with an agentic workflow. When a machine fails, AI agents gather information from MES, production planning, maintenance and documentation, evaluate options (e.g. wait for the repair or move orders to an alternative machine) and present them to the responsible roles for approval. Only after human approval are the maintenance order and the rescheduling executed in the target systems. Decision-relevant figures come from traceable rules, not from the language model; every step can be interrupted and resumed and is fully logged. You provide the domain and statistical foundation: how long a repair realistically takes, how certain that estimate is, and by which rules options are allowed or excluded.
Responsibilities
- Develop rules for repair time estimation from the failure history (comparable cases, spare part and staff availability), including an uncertainty band and default assumptions when history is missing
- Assess metrics such as MTTR and MTBF and their validity with few cases
- Agree rules for admissible options and exclusions with the maintenance and production planning roles
- Create realistic failure cases and data sets for tests and demos
- Review the agents' results from a domain perspective and help define acceptance criteria
- Later: propose metrics for measuring benefits (e.g. time from failure report to action)
Must have
- At least 5 years of experience in maintenance, reliability engineering or asset management in manufacturing
- Confident use of statistical methods on failure and repair data (e.g. distributions, confidence or prediction intervals, Weibull analysis)
- Experience with maintenance data from a CMMS or ERP (e.g. SAP PM, IBM Maximo)
- Fluent English
- At least 4 hours of daily overlap with Central European Time (CET)
Nice to have
- Methods such as RCM, FMEA, RCA
- Python or R for data analysis
- Knowledge of production planning and scheduling
- Experience with predictive maintenance or digitalization projects
- CMRP or Six Sigma Green/Black Belt certification
- German language skills
Terms
- Location: 100% remote
- Duration: typically 3 to 6 months, extension possible
- Start: depending on the project, from Q1 2027
- Contract: freelance, invoicing in EUR
Application
Please answer briefly:
- In which manufacturing environment did you most recently analyze failure and repair data (industry, equipment, methods, result)?
- How would you estimate a repair time with an uncertainty band when there are only a few comparable failures for a machine?
- What is your hourly rate in EUR, how many hours per week can you offer, where are you based (country and time zone), and when are you available?
In email communication please always include the project ID of this ad. Please do not call unless an appointment has been arranged.