Senior ML Ops Engineer

What you’ll doAt 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 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 teamShare & advocate your work with the tech communityOur stackProgramming languages : Python / Pyspark / SQL.Cloud providers : AWS / Azure.Machine Learning platform : AWS SageMakerContainer / Orchestration : AWS ECS / DockerData warehouse / storage : AWS S3 / AWS RedshiftDatabases : PostgreSQLSearch engine : ElasticSearchData capture tool : AWS KinesisML pipeline : AWS Step Functions / AWS Lambda / AWS SageMaker.Infrastructure as code : TerraformAnd any other tool you deem relevantWho you areIf you don’t meet all the requirements below but believe this opportunity matches your expectations and experience, we still encourage you to applyYou could be our next team mate if you:Have a good team spirit, enjoy learning new skills and have a strong sense of initiativeExcellent 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 preferredIdealy 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 / TerraformGood understanding of machine learning algorithms / concepts / trendsFirst experience in Deep Learning Framework, preferably PyTorchKnowledge 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, PEFTHave experience in building MLOps pipelines for containerizing models and solutions with DockerNow, it would be fantastic if you:Have experience with Kubernetes, GitOps tools (e.g. ArgoCD) and/or KafkaHave experience with Terraform Enterprise / Hashicorp Vault / Cloudflare is a plusHave experience developing in Javascript / Typescript and using deployment in the browser (transformers.js / langchain.js)Have experience with ML model quantization and optimizationWhat we offerFree health insurance for you and your childrenParent Care Program: receive one additional month of leave on top of the legal parental leaveFree mental health and coaching services through our partner Moka.careFor caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological supportWork from EU countries and the UK for up to 10 days per year, thanks to our flexibility days policyWork Council subsidy to refund part of sport club membership or creative classUp to 14 days of RTTA subsidy from the work council to refund part of the membership to a sport club or a creative classLunch voucher with Swile cardThe interview processHR 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 checkA copy of your criminal records (“extrait de casier judiciaire B3”)Job detailsPermanent positionFull TimeWorkplace : Paris areaStart date: asapRemuneration : fix + bonus on objectives (according to your profile)At Doctolib, we believe in improving access to healthcare for everyone - regardless of where you come from, what you look like. This translates into our recruitment process: Doctolib is an equal opportunity employer. We don't just accept diversity at Doctolib, we respect and celebrate itThe 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, or place of origin. If you have a disability, let us know if there's any way we can make the interview process smoother for youAll the information transmitted via this form is processed by Doctolib for the purpose of managing applications. For more information on how Doctolib processes your application data, click here .If you wish to exercise your rights or if you have any questions about the processing of your data, you can write to us at hr.dataprivacy(at)doctolib.com . #J-18808-Ljbffr