About eComID

eComID is building the Shopping Passport for modern commerce – a shopper context layer that enables brands to understand and personalize every shopper from their very first visit. By combining AI-powered sizing, conversational search and intelligent product discovery, we're making online shopping smarter, more personal and less wasteful.

Launched in Stockholm in 2024, eComID is already operating at scale, reaching more than 20 million shoppers every month. We recently raised a $17.4 million seed round – the largest in European fashion-tech history and the second-largest globally.

We're backed by H&M Group, Stadium, leading international VCs and some of Europe's most successful industry leaders and technology founders, including Helena Helmersson, Sebastian Knutsson, Maria Raga and Alan Mamedi.

Now, we're bringing together exceptional people with warm hearts to build a world-class fashion-tech company from Stockholm. If you want to solve meaningful problems, move fast and help shape how the world shops, this is the place to do it.

What you will work on

We are looking for a Senior MLOps & Cloud Infrastructure Engineer to help build, operate, and evolve the cloud infrastructure and engineering platforms behind our production AI systems.

You will sit at the intersection of cloud infrastructure, DevOps, platform engineering, and machine learning. Your job is to ensure our services, data pipelines, and AI models run reliably and efficiently at scale, while giving our engineers the tools and infrastructure they need to build, deploy, and iterate quickly.

This is a hands-on engineering role with broad ownership across our infrastructure. You will work on everything from cloud architecture, container orchestration, networking, and deployment automation to model serving, GPU workloads, and ML pipelines.

You won't be expected to develop the machine learning models themselves, but you will work closely with the engineers building them, understanding their needs and translating those into reliable, scalable production systems.

You will help build and own our cloud infrastructure, deployment platforms, and ML operations, including:

• Cloud infrastructure and architecture: Designing, provisioning, and operating scalable infrastructure on GCP, with a focus on reliability, security, performance, and cost.

• Kubernetes and container orchestration: Managing containerised applications, services, and ML workloads, including deployment, scaling, resource allocation, and orchestration.

• Infrastructure as code: Building reproducible, automated infrastructure and environment management using tools such as Terraform.

• CI/CD and developer platforms: Designing and improving automated build, testing, deployment, and release workflows for backend services, data pipelines, and machine learning systems.

• Production operations and reliability: Improving service availability, resilience, incident response, disaster recovery, and operational practices across our platform.

• Observability and monitoring: Building logging, metrics, tracing, dashboards, and alerting across infrastructure, applications, data pipelines, and model inference.

• Networking and security: Working with cloud networking, service connectivity, IAM, secrets management, and secure communication between services.

• ML infrastructure and model serving: Building reliable infrastructure for deploying, scaling, and operating machine learning models in production, including real-time and batch inference.

• Model lifecycle and deployment: Improving how models move from experimentation and training to production, including versioning, validation, rollbacks, and reproducibility.

• Training and GPU workloads: Supporting distributed processing, model training, GPU scheduling, and efficient resource utilisation for computationally intensive workloads.

• Performance and cost optimisation: Identifying and resolving bottlenecks across cloud services, compute, storage, networking, and inference workloads.

• Internal tooling and automation: Building self-service tools and abstractions that make it easier for engineers to provision infrastructure, deploy applications, run experiments, and operate production systems.

• Architecture and scalability: Helping shape how our infrastructure evolves as our traffic, data volumes, AI workloads, and engineering team grow.

You will work closely with both our AI and software engineering teams, identifying where infrastructure is limiting development speed, reliability, or scalability, and turning those challenges into better systems, tooling, and engineering practices.

What we are looking for

We are looking for a strong cloud infrastructure and software engineer with DevOps experience and a solid understanding of machine learning systems in production.

You should be comfortable taking ownership of infrastructure end to end, from architecture and provisioning to deployment, monitoring, troubleshooting, and optimisation.

You should have experience with several of the following:

• Designing, deploying, and operating production infrastructure on a major cloud provider, ideally GCP

• Kubernetes, Docker, and container orchestration in production environments

• Infrastructure as code, ideally Terraform, and automated environment management

• Building and maintaining CI/CD pipelines for backend services and infrastructure

• Cloud networking, load balancing, service discovery, DNS, IAM, and security fundamentals

• Monitoring, logging, tracing, alerting, and production observability

• Linux systems, networking, and troubleshooting distributed applications

• Building reliable, highly available services and handling production incidents

• Python, Go, or other languages used for backend engineering, automation, and infrastructure tooling

• Operating distributed systems, asynchronous workloads, and data processing pipelines

• Cloud cost management, resource allocation, and performance optimisation

• Deploying and operating machine learning models in production

• Real-time and batch inference infrastructure, including low-latency model serving

• Machine learning pipelines, model versioning, experiment tracking, and reproducibility

• GPU infrastructure, resource scheduling, and inference optimisation

• Designing internal developer platforms, automation, or self-service engineering tools

Experience with production machine learning systems is important, but you do not need to be a machine learning researcher or model developer.

We want someone who understands how ML workloads differ from traditional services, can collaborate effectively with AI engineers, and knows how to build infrastructure that supports training, experimentation, deployment, and inference.

We care more about strong engineering fundamentals, sound architectural judgement, and experience operating real systems than whether you have used our exact stack before.

Our current tech stack

Today, our core stack includes:

Languages: Python, Go, TypeScript

Cloud & Infrastructure: Google Cloud Platform (GCP), Kubernetes, Docker, Cloud Run, Terraform

Data & Storage: BigQuery, ClickHouse, PostgreSQL, Redis, Milvus, Typesense

ML & Data Processing: PyTorch, Kubeflow, Feast, Apache Beam

Services & Communication: ConnectRPC, HTTP APIs, asynchronous messaging

DevOps & Automation: GitHub Actions, CI/CD pipelines, infrastructure automation

We are not attached to technologies for their own sake and are open to introducing better tools where they make sense.

What we value

We are looking for someone who:

• Takes ownership of the systems they build, from initial design to long-term production operations

• Has strong infrastructure fundamentals and enjoys understanding how systems work under the hood

• Cares deeply about reliability, observability, security, and maintainability

• Is comfortable working across cloud infrastructure, networking, containers, applications, and ML workloads

• Understands the trade-offs between scalability, latency, reliability, cost, and developer experience

• Enjoys automating repetitive work and building tools that make other engineers more productive

• Can investigate complex production issues across multiple layers of a distributed system

• Thinks proactively about failure modes, capacity planning, recovery, and operational complexity

• Prefers simple, robust architectures over unnecessary abstractions and infrastructure complexity

• Is comfortable questioning existing technical decisions and proposing better approaches

• Understands that infrastructure should enable fast development rather than become a bottleneck

• Enjoys working closely with AI engineers, data engineers, and backend engineers

• Appreciates the specific challenges of running machine learning workloads in production

• Treats internal platforms and developer tooling as products that should be reliable and easy to use

• Knows when to introduce new infrastructure capabilities and when existing solutions are enough

• Balances long-term engineering quality with the speed and pragmatism required in a growing startup

What we offer

• A best-in-class team with warm hearts and no ego. We care deeply about the quality of our work, while staying kind, humble, and easy to work with. The goal is always to build the best product possible, not to win internal arguments.

• Real ownership of our infrastructure and platform. You will have meaningful influence over our cloud architecture, infrastructure, deployment systems, and operational practices. We expect senior engineers to shape how we build and operate systems, not just maintain what already exists.

• A direct connection to product. Your work will have a direct impact on the performance, reliability, and capabilities of products used by millions of shoppers.

• Work directly with the AI and engineering teams. You will help bridge the gap between software development, machine learning, and production infrastructure, enabling engineers to build and ship with confidence.

• Build infrastructure that directly accelerates innovation. Improvements to deployment automation, cloud architecture, developer tooling, and ML infrastructure will directly affect how quickly we can experiment, develop, and deliver new capabilities.

• Interesting technical problems at real scale. You will work on distributed systems, high-throughput data processing, real-time inference, and cloud infrastructure where performance, reliability, and efficiency genuinely matter.

• Freedom to choose the right tools. We have a strong existing stack, but no attachment to technology for its own sake. If there is a better way to solve a problem, we want to hear it.

• High standards without unnecessary process. We care about reliability, thoughtful engineering, and maintainable systems while keeping teams small, communication direct, and decision-making fast.

• The chance to shape the next stage of the company. We are still early enough that the architecture, infrastructure, and engineering practices you establish now will have a lasting impact on how eComID builds and operates its products and AI systems.

Benefits

• Health allowance: 5,000 SEK wellness allowance per year

• Office: Work from our office in central Stockholm

• Office perks: Weekly breakfasts and snacks throughout the day

• Tech: Your choice of computer, phone, and phone contract

• Learning & growth: Annual budget for courses, books, and conferences

• Insurance: Extensive coverage, including private healthcare insurance

• Pension: Private pension insurance via SPP