Senior MLOps Engineer
Lynceus

Lynceus

Senior MLOps Engineer

The company

Lynceus

Lynceus

  • Software, Artificial Intelligence / Machine Learning, SaaS / Cloud Services
  • < 15 employees

The job

Senior MLOps Engineer

Who are they?

Who we are

At Lynceus, we are building a predictive system for high-value manufacturing.

Our platform leverages manufacturing data and in-house process comprehension to predict the performance of high-value products (in-line and end-of-line quality tests), enabling engineers to manage their manufacturing processes in real-time. We provide gains for our customers both through yield improvement and added production capacity.

Our vision is to use our core quality/performance prediction product as a base for a comprehensive, predictive system for high value manufacturing.

We are deployed and have secured contracts with several major semiconductor companies, our first target market. We are also relevant for most high added-value manufacturing, with use cases and interest proven in LED, battery, biopharma and chemical industries.

We are venture-backed and supported by prominent players of both enterprise software and semiconductor industries. As we scale our team, we are actively looking for enthusiastic, ambitious and driven individuals to help us support our growth.

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Job description

What you will do

As a senior MLOps engineer, you will join the Lead MLOps to:

  • Own the machine learning workflows supporting our platform in the cloud and at the edge in factories
  • Collaborate with our Machine Learning team to accelerate the integration of new industrial processes to our product
  • Use our platform to automate model training, evaluation and deployment directly in semiconductor factories
  • Build highly available and elastic pipelines using our AWS and on-premises Kubernetes clusters
  • Build from scratch and use the latest technologies fit for the job like MLFlow
  • Promote a data driven and data quality culture
  • Spread good MLOps practices in the teams by trainings, workshops and processes definition

Preferred experience

What we’re looking for

  • Experience within or working very closely with a Data Science team
  • Experience following software engineering practices to deliver code used internally or externally
  • Several machine learning models put in production and maintained
  • Good understanding of data science principles
  • Ability in at least one scripting language, preferably python
  • Version control (git favoured) and docker knowledge
  • Software development and delivery practices
  • A mindset that is: Agile, Iterative, DevOps and value-driven
  • Ability to receive and integrate feedback
  • Good communication & teamwork
  • Interest in training and helping others
  • Ability to take the lead on important projects and be proactive
  • Fluency in english

What would be nice

  • Strong data manipulation skills (pandas)
  • Deep learning knowledge
  • Scikit-learn advanced use (pipelines and transformers)
  • Kubernetes regular use
  • Ease to work in a cloud environment (we use AWS)
  • MLOps tools (flow, project setup, model storage, model serving)
  • CI/CD setup
  • Automated testing proficiency
  • Experience in providing and maintaining (internally or externally) a library (preferably python)
  • Familiar with implementing and maintaining HTTP REST services (preferably fastAPI)
  • Interest in software craft practices and architecture patterns
  • Past experiences of working with data at factories

Recruitment process

  1. A 30 min screening call with the Lead MLops to meet each other and see if you match
  2. A technical assesment to do at home that should not take you more than one hour
  3. A technical interview with several Lynceus employees among Data engineers, scientists, the engineering lead and the VP product. You shall discuss your assesment as well as other MLOps topics
  4. A final culture fit interview with Lynceus founders
Lynceus

Lynceus

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