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Senior ML Ops Engineer

il y a 7 heures

Paris, Île-de-France Doctolib Temps plein
What you'll do

At Doctolib, we're on a mission to transform the way healthcare is delivered by leveraging the power of AI.

We are seeking a highly skilled, motivated, and collaborative Senior MLOps Engineer to join our ML Platform Team. The successful candidate will play a pivotal role in developing, deploying, and maintaining machine learning models and systems, ensuring their performance and scalability. You will collaborate with data scientists, software engineers, platform engineers and other stakeholders to deliver high-quality solutions that power Doctolib products for care teams and patients.

Your responsibilities include but are not limited to:

  • Collaborate with data scientists and engineers to develop and deploy machine learning models, ensuring they meet performance, scalability, and reliability requirements.
  • Implement and maintain the MLOps pipeline, including version control, continuous integration, continuous deployment, and monitoring of machine learning models.
  • Develop tools, frameworks, and best practices to streamline the model development and deployment process.
  • Ensure the availability, reliability, and performance of machine learning models and systems, proactively addressing any issues that arise.
  • Monitor the performance of machine learning models in production, identifying areas for improvement and working with data scientists to optimize the models.
  • Stay up-to-date with the latest advancements in MLOps and machine learning technologies, incorporating them into the ML Platform Team's workflows as appropriate.
  • Collaborate with cross-functional teams to gather requirements, provide technical guidance, and contribute to the development of machine learning solutions.
  • Document MLOps processes, standards, and best practices to ensure knowledge transfer and consistency across the team
  • Share & advocate your work with the tech community

Our stack

  • Programming languages : Python / Pyspark / SQL.  
  • Cloud providers : AWS / Azure. 
  • Machine Learning platform : AWS SageMaker
  • Container / Orchestration : AWS ECS / Docker
  • Data warehouse / storage : AWS S3 / AWS Redshift
  • Databases : PostgreSQL
  • Search engine : ElasticSearch
  • Data capture tool : AWS Kinesis
  • ML pipeline : AWS Step Functions / AWS Lambda / AWS SageMaker. 
  • Infrastructure as code : Terraform

And any other tool you deem relevant

Who you are

If you don't meet all the requirements below but believe this opportunity matches your expectations and experience, we still encourage you to apply

You could be our next team mate if you: 

  • Have a good team spirit, enjoy learning new skills and have a strong sense of initiative 
  • Excellent communication and collaboration skills, with the ability to work well in cross-functional teams and write clear documentation. 
  • Have a Bachelor's degree in Computer Science, Engineering, or a related field; advanced degree preferred
  • Idealy a first experience in MLOps Engineer, Cloud engineer for Machine Learning applications or similar role. 
  • Are proficient in our core languages : Python / SQL / Shell Scripting / Terraform
  • Good understanding of machine learning algorithms / concepts / trends
  • First experience in Deep Learning Framework, preferably PyTorch
  • Knowledge of cloud platforms like AWS and services like Amazon SageMaker, EC2, ECS, S3, CloudWatch and/or Azure and GCP equivalents.
  • Interest in building with HuggingFace Technologies, including Transformers, Diffusers, Accelerate, PEFT
  • Have experience in building MLOps pipelines for containerizing models and solutions with Docker

Now, it would be fantastic if you: 

  • Have experience with Kubernetes, GitOps tools (e.g. ArgoCD) and/or Kafka
  • Have experience with Terraform Enterprise / Hashicorp Vault / Cloudflare is a plus 
  • Have experience developing in Javascript / Typescript and using deployment in the browser / )
  • Have experience with ML model quantization and optimization
What we offer
  • Free health insurance for you and your children
  • Parent Care Program: receive one additional month of leave on top of the legal parental leave
  • Free mental health and coaching services through our partner
  • For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
  • Work from EU countries and the UK for up to 10 days per year, thanks to our flexibility days policy
  • Work Council subsidy to refund part of sport club membership or creative class
  • Up to 14 days of RTT
  • A subsidy from the work council to refund part of the membership to a sport club or a creative class 
  • Lunch voucher with Swile card
The interview process 
  • HR interview by phone (45 minutes)
  • Hiring manager interview (1 hour)
  • Case study & case restitution (1 hour)
  • Behavioral interview / Meet the team session (1 hour / half day immersion)
  • At least one reference check 
  • A copy of your criminal records ("extrait de casier judiciaire B3")
Job details
  • Permanent position
  • Full Time 
  • Workplace : Paris area
  • Start date: asap
  • Remuneration : fix + bonus on objectives (according to your profile)
If you would like to find out more about tech life at Doctolib, feel free to read our latest Medium blog articles
 
At Doctolib, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination.
 
The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, disability.
 
To ensure equal opportunities, we invite you to exclude personal information (e.g. pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process.
 
Join us in building the healthcare we all dream of
 
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