Senior MLOps Engineer (m/w/d)

Full opportunity description

Short Facts • Location: Munich, Germany

• Employment Type: Full-Time, indefinite term

• Salary Range: € 95,000 – 115,000 per year gross, depending on seniority level

• Office First work setup

• Language Requirement: C1 Level English

Your Responsibilities

• Act as the technical bridge between data science and software engineering, helping research models become reliable, maintainable production systems, and helping engineering understand what ML workloads actually need

• Design and build the data and feature pipelines that support Flexa's forecasting and trading models at scale across hundreds of thousands of distributed systems

• Leverage Flexa’s deployment, orchestration, and serving platform to bring models into production, for both batch and real-time workloads

• Establish monitoring and observability for models in production, like drift, data quality, latency, and failure modes

• Partner closely with data scientists on model design and validation, bringing an engineering perspective on scalability, maintainability, and production risk from early on

• Champion engineering rigor and ML best practices to foster an open, data-driven engineering culture.

• Contribute to the technical roadmap, anticipating scaling needs as data volume and model complexity grow

• Opportunity to guide and develop more junior colleagues through design review, code review, and structured feedback

• Be part of a cross-functional team of data scientists, software engineers, and other teams across Flexa's partner ecosystem

Your Profile Mandatory Requirements • University degree in an engineering or analytical field (Computer Science, Mathematics, Physics, Statistics, Engineering or a related discipline)

• 5+ years of engineering experience, with significant time spent supporting or building ML systems in production

• Proficiency in Python and software engineering best practices: testing, code quality, code review, CI/CD, monitoring, and modular code design

• Solid working knowledge of MLOps practices: pipeline setup, deployment, monitoring

• Enough fluency in ML/statistical modeling to collaborate effectively with data scientists and make sound architectural tradeoffs together

• Independent, pragmatic problem-solving with strong attention to detail in a fast-paced environment

• Excellent English communication and interpersonal skills

• Cross-functional collaboration mindset across data scientists, software engineers, and partner-company stakeholders

Skills to Set You Apart • Experience in energy, power markets, or other near-real-time operational domains

• Familiarity with orchestration tools (Airflow or similar), MLOps toolchains (MLflow, Sagemaker, or similar), and streaming systems (Kafka or similar)

• Hands-on experience with large-scale data tooling: Spark, Dask, or comparable frameworks

• Experience designing or owning near-real-time analytics and/or ML workflows, including observability

• Track record of taking models from research into production on AWS or comparable cloud provider

This won’t be the right role for you if… • You don’t have the habit of defining your own tasks and have a preference for working in clearly separated functions

Benefits • Virtual Share Options: we offer virtual share options to all our employees

• Professional Development: annual development budget of €3,000 for coachings, trainings, books, and similar

• Health & Sport Subsidy: company-subsidised sports facilities membership, or Public Transportation Subsidy

• Lunch/Dinner Allowance Vouchers: allowance for meals on working days as digital meal vouchers

• Work Equipment: MacBook or Windows laptop, iPhone (also for private use), and an ergonomic workplace setup with company-funded access to leading AI developer tools

• Regular Team Events: knowledge sessions, afterwork, sports, offsites, Halloween, Pride Month, and more

A Short Note from Your Future Lead Willi Richert, VP of Technology — flexa

Hi there! I'm Willi, VP of Technology at flexa. I've spent the last 15 years building engineering teams around systems that have to make good decisions fast and at scale. Most recently at Lyft, where I led the mapping organization — 30+ engineers across five countries — and we moved more than 96% of rides off Google Maps onto our own mapping product. Before that I worked on machine learning and conversational AI at Microsoft Bing, and I did a PhD on learning in heterogeneous robot groups, which is a long way of saying that distributed decision-making has held my attention for a while.

What pulled me to flexa is that it's the same class of problem with something physical at the other end. We dispatch energy of the Enpal customer fleet in real time against energy markets. If our infrastructure is a few seconds late or a few percent off, customers lose money and the grid gets less flexibility than it could have had. That makes cloud engineering a first-order product concern here rather than a supporting function — which is why this role sits close to the decisions that actually matter.

How I work: I'd rather hand you the whole problem, context and constraints included, than a ticket. I care that engineers can see the consequences of what they build, and I'll be direct with feedback and expect the same back — the fastest way to lose a year is for everyone to stay polite about an architecture that isn't working.

You won't find everything already built. Some of it is greenfield, some of it needs replacing, and you'll have real influence over which is which. If that sounds like your kind of problem, I'd like to hear from you — and if you're not sure your profile is a perfect match, apply anyway and let's talk.

Looking forward to meeting you, Willi

Your Contact in our People Team

Your Responsibilities

• Act as the technical bridge between data science and software engineering, helping research models become reliable, maintainable production systems, and helping engineering understand what ML workloads actually need Act as the technical bridge between data science and software engineering, helping research models become reliable, maintainable production systems, and helping engineering understand what ML workloads actually need • Design and build the data and feature pipelines that support Flexa's forecasting and trading models at scale across hundreds of thousands of distributed systems Design and build the data and feature pipelines that support Flexa's forecasting and trading models at scale across hundreds of thousands of distributed systems • Leverage Flexa’s deployment, orchestration, and serving platform to bring models into production, for both batch and real-time workloads Leverage Flexa’s deployment, orchestration, and serving platform to bring models into production, for both batch and real-time workloads • Establish monitoring and observability for models in production, like drift, data quality, latency, and failure modes Establish monitoring and observability for models in production, like drift, data quality, latency, and failure modes • Partner closely with data scientists on model design and validation, bringing an engineering perspective on scalability, maintainability, and production risk from early on Partner closely with data scientists on model design and validation, bringing an engineering perspective on scalability, maintainability, and production risk from early on • Champion engineering rigor and ML best practices to foster an open, data-driven engineering culture. Champion engineering rigor and ML best practices to foster an open, data-driven engineering culture. • Contribute to the technical roadmap, anticipating scaling needs as data volume and model complexity grow Contribute to the technical roadmap, anticipating scaling needs as data volume and model complexity grow • Opportunity to guide and develop more junior colleagues through design review, code review, and structured feedback Opportunity to guide and develop more junior colleagues through design review, code review, and structured feedback • Be part of a cross-functional team of data scientists, software engineers, and other teams across Flexa's partner ecosystem Be part of a cross-functional team of data scientists, software engineers, and other teams across Flexa's partner ecosystem

This won’t be the right role for you if…

• You don’t have the habit of defining your own tasks and have a preference for working in clearly separated functions You don’t have the habit of defining your own tasks and have a preference for working in clearly separated functions

Mandatory Requirements

• University degree in an engineering or analytical field (Computer Science, Mathematics, Physics, Statistics, Engineering or a related discipline) University degree in an engineering or analytical field (Computer Science, Mathematics, Physics, Statistics, Engineering or a related discipline) • 5+ years of engineering experience, with significant time spent supporting or building ML systems in production 5+ years of engineering experience, with significant time spent supporting or building ML systems in production • Proficiency in Python and software engineering best practices: testing, code quality, code review, CI/CD, monitoring, and modular code design Proficiency in Python and software engineering best practices: testing, code quality, code review, CI/CD, monitoring, and modular code design • Solid working knowledge of MLOps practices: pipeline setup, deployment, monitoring Solid working knowledge of MLOps practices: pipeline setup, deployment, monitoring • Enough fluency in ML/statistical modeling to collaborate effectively with data scientists and make sound architectural tradeoffs together Enough fluency in ML/statistical modeling to collaborate effectively with data scientists and make sound architectural tradeoffs together • Independent, pragmatic problem-solving with strong attention to detail in a fast-paced environment Independent, pragmatic problem-solving with strong attention to detail in a fast-paced environment • Excellent English communication and interpersonal skills Excellent English communication and interpersonal skills • Cross-functional collaboration mindset across data scientists, software engineers, and partner-company stakeholders Cross-functional collaboration mindset across data scientists, software engineers, and partner-company stakeholders

Benefits

• Virtual Share Options: we offer virtual share options to all our employees Virtual Share Options: we offer virtual share options to all our employees • Professional Development: annual development budget of €3,000 for coachings, trainings, books, and similar Professional Development: annual development budget of €3,000 for coachings, trainings, books, and similar • Health & Sport Subsidy: company-subsidised sports facilities membership, or Public Transportation Subsidy Health & Sport Subsidy: company-subsidised sports facilities membership, or Public Transportation Subsidy • Lunch/Dinner Allowance Vouchers: allowance for meals on working days as digital meal vouchers Lunch/Dinner Allowance Vouchers: allowance for meals on working days as digital meal vouchers • Work Equipment: MacBook or Windows laptop, iPhone (also for private use), and an ergonomic workplace setup with company-funded access to leading AI developer tools Work Equipment: MacBook or Windows laptop, iPhone (also for private use), and an ergonomic workplace setup with company-funded access to leading AI developer tools • Regular Team Events: knowledge sessions, afterwork, sports, offsites, Halloween, Pride Month, and more Regular Team Events: knowledge sessions, afterwork, sports, offsites, Halloween, Pride Month, and more

Short Facts

• Location: Munich, Germany Location: Munich, Germany • Employment Type: Full-Time, indefinite term Employment Type: Full-Time, indefinite term • Salary Range: € 95,000 – 115,000 per year gross, depending on seniority level Salary Range: € 95,000 – 115,000 per year gross, depending on seniority level • Office First work setup Office First work setup • Language Requirement: C1 Level English Language Requirement: C1 Level English

Skills to Set You Apart

• Experience in energy, power markets, or other near-real-time operational domains Experience in energy, power markets, or other near-real-time operational domains • Familiarity with orchestration tools (Airflow or similar), MLOps toolchains (MLflow, Sagemaker, or similar), and streaming systems (Kafka or similar) Familiarity with orchestration tools (Airflow or similar), MLOps toolchains (MLflow, Sagemaker, or similar), and streaming systems (Kafka or similar) • Hands-on experience with large-scale data tooling: Spark, Dask, or comparable frameworks Hands-on experience with large-scale data tooling: Spark, Dask, or comparable frameworks • Experience designing or owning near-real-time analytics and/or ML workflows, including observability Experience designing or owning near-real-time analytics and/or ML workflows, including observability • Track record of taking models from research into production on AWS or comparable cloud provider Track record of taking models from research into production on AWS or comparable cloud provider

A Short Note from Your Future Lead

Willi Richert, VP of Technology — flexa Hi there! I'm Willi, VP of Technology at flexa. I've spent the last 15 years building engineering teams around systems that have to make good decisions fast and at scale. Most recently at Lyft, where I led the mapping organization — 30+ engineers across five countries — and we moved more than 96% of rides off Google Maps onto our own mapping product. Before that I worked on machine learning and conversational AI at Microsoft Bing, and I did a PhD on learning in heterogeneous robot groups, which is a long way of saying that distributed decision-making has held my attention for a while. What pulled me to flexa is that it's the same class of problem with something physical at the other end. We dispatch energy of the Enpal customer fleet in real time against energy markets. If our infrastructure is a few seconds late or a few percent off, customers lose money and the grid gets less flexibility than it could have had. That makes cloud engineering a first-order product concern here rather than a supporting function — which is why this role sits close to the decisions that actually matter. How I work: I'd rather hand you the whole problem, context and constraints included, than a ticket. I care that engineers can see the consequences of what they build, and I'll be direct with feedback and expect the same back — the fastest way to lose a year is for everyone to stay polite about an architecture that isn't working. You won't find everything already built. Some of it is greenfield, some of it needs replacing, and you'll have real influence over which is which. If that sounds like your kind of problem, I'd like to hear from you — and if you're not sure your profile is a perfect match, apply anyway and let's talk. Looking forward to meeting you, Willi

Über uns

Flexa, a Joint Venture between Enpal and Entrix, is chartered with delivering a Virtual Power Plant (VPP) delivering exceptional energy cost savings while supporting the transition to a 100% renewable electricity future. The combination of delivering complete residential energy systems at great cost with savvy market participation in several revenue streams sets us up to deliver real world customer savings while improving customer satisfaction enjoying the advantages of a fully electrified and energy producing home. Through a deep integration into the installed hardware and a direct connection to the customer interfaces, Flexa’s solution controls the energy assets (such as EV, heat pump, home storage and others) of the entire Enpal energy community with an exceptional level of accuracy, speed, transparency, and thus customer satisfaction. With intelligent real-time dispatching algorithms, Flexa maximizes Enpal customers’ usable flexibility and its returns on energy markets including costs, such as grid fees, asset degradation.

• University degree in an engineering or analytical field (Computer Science, Mathematics, Physics, Statistics, Engineering or a related discipline) University degree in an engineering or analytical field (Computer Science, Mathematics, Physics, Statistics, Engineering or a related discipline) • 5+ years of engineering experience, with significant time spent supporting or building ML systems in production 5+ years of engineering experience, with significant time spent supporting or building ML systems in production • Proficiency in Python and software engineering best practices: testing, code quality, code review, CI/CD, monitoring, and modular code design Proficiency in Python and software engineering best practices: testing, code quality, code review, CI/CD, monitoring, and modular code design • Solid working knowledge of MLOps practices: pipeline setup, deployment, monitoring Solid working knowledge of MLOps practices: pipeline setup, deployment, monitoring • Enough fluency in ML/statistical modeling to collaborate effectively with data scientists and make sound architectural tradeoffs together Enough fluency in ML/statistical modeling to collaborate effectively with data scientists and make sound architectural tradeoffs together • Independent, pragmatic problem-solving with strong attention to detail in a fast-paced environment Independent, pragmatic problem-solving with strong attention to detail in a fast-paced environment • Excellent English communication and interpersonal skills Excellent English communication and interpersonal skills • Cross-functional collaboration mindset across data scientists, software engineers, and partner-company stakeholders Cross-functional collaboration mindset across data scientists, software engineers, and partner-company stakeholders