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Senior MLOps Specialist
Il y a 3 mois
Key Responsibilities
At Doctolib, our goal is to revolutionize healthcare delivery through innovative AI solutions.
We are in search of a talented, driven, and team-oriented MLOps Specialist to become a vital member of our Machine Learning Platform Team. The ideal candidate will be instrumental in the creation, deployment, and upkeep of machine learning models and systems, ensuring optimal performance and scalability. You will work closely with data scientists, software developers, platform engineers, and other key stakeholders to provide high-quality solutions that enhance Doctolib's offerings for healthcare professionals and patients.
Your duties will encompass, but are not limited to:
- Partnering with data scientists and engineers to design and implement machine learning models that fulfill performance, scalability, and reliability standards.
- Establishing and managing the MLOps pipeline, which includes version control, continuous integration, continuous deployment, and monitoring of machine learning models.
- Creating tools, frameworks, and best practices to optimize the model development and deployment workflow.
- Ensuring the availability, reliability, and performance of machine learning models and systems, proactively resolving any issues that may arise.
- Tracking the performance of machine learning models in production, identifying opportunities for enhancement, and collaborating with data scientists to refine the models.
- Keeping abreast of the latest developments in MLOps and machine learning technologies, integrating them into the ML Platform Team's processes as needed.
- Working with cross-functional teams to gather requirements, provide technical insights, and contribute to the development of machine learning solutions.
- Documenting MLOps procedures, standards, and best practices to ensure knowledge sharing and consistency across the team.
- Promoting and sharing your work with the technical community.
Technology Stack
- Programming Languages: Python, Pyspark, SQL.
- Cloud Platforms: AWS, Azure.
- Machine Learning Platform: AWS SageMaker.
- Containerization/Orchestration: AWS ECS, Docker.
- Data Storage: AWS S3, AWS Redshift.
- Databases: PostgreSQL.
- Search Technologies: ElasticSearch.
- Data Capture: AWS Kinesis.
- ML Pipeline: AWS Step Functions, AWS Lambda, AWS SageMaker.
- Infrastructure as Code: Terraform.
Candidate Profile
If you feel that you align with the qualifications below, we encourage you to consider this opportunity:
- Possess a collaborative spirit, a passion for learning new skills, and a proactive mindset.
- Exhibit excellent communication and teamwork abilities, with a knack for clear documentation.
- Hold a Bachelor's degree in Computer Science, Engineering, or a related discipline; an advanced degree is preferred.
- Bring at least 5 years of experience as an MLOps Engineer, Cloud Engineer for Machine Learning applications, or a similar role.
- Demonstrate proficiency in core programming languages: Python, SQL, Shell Scripting, Terraform.
- Have a solid understanding of machine learning algorithms, concepts, and trends.
- Possess expertise in Deep Learning Frameworks, preferably PyTorch.
- Show strong familiarity with cloud platforms like AWS and services such as Amazon SageMaker, EC2, ECS, S3, CloudWatch, and/or their equivalents in Azure and GCP.
- Have experience with HuggingFace Technologies, including Transformers, Diffusers, Accelerate, and PEFT.
- Be skilled in building MLOps pipelines for containerizing models and solutions using Docker.
- Have experience with MLOps Frameworks like MLFlow or Kubeflow.
- Exhibit proficiency in traditional DevOps practices, including setting up CI/CD systems with declarative pipelines (e.g., GitHub Actions), monitoring, and managing development, QA, and production environments.
- Be familiar with version control and application/library packaging tools (e.g., Git, Poetry, Docker).
- Have knowledge of Apache Spark or other big data processing frameworks.
- Possess experience with GPU programming (CUDA) and optimization for cost and performance.
- Have familiarity with back-end and middleware services, such as NGINX, FastAPI, or Airflow.
Preferred Qualifications
- Experience with Kubernetes, GitOps tools (e.g., ArgoCD), and/or Kafka.
- Familiarity with Terraform Enterprise, Hashicorp Vault, or Cloudflare is a plus.
- Experience in JavaScript or TypeScript development and browser deployment.
- Knowledge of ML model quantization and optimization techniques.
What We Provide
- A stock-option program for all employees.
- Quarterly bonuses and a competitive compensation package.
- A comprehensive onboarding program - the Doctolib Academy.
- Ongoing training programs covering essential skills (English, soft skills, technical expertise).
- Transparent internal mobility opportunities.
- Two days per year dedicated to supporting health charities and making a positive social impact.
- Mental health and wellbeing initiatives.
- The Doctolib Parent Care Program, which includes extended parental leave and community events.
- High-quality office environments that promote collaboration and wellbeing.
- Subsidies for sports club memberships or creative classes.
- Comprehensive health insurance fully covered by the company.
- Meal subsidies and various food options available in our offices.
- A flexible workplace policy that accommodates both hybrid and office-based work.
- Flexibility to work in EU countries and the UK for up to 10 days per year.
- Support for relocation for international moves and new hires.
Interview Process
- Initial HR interview via phone (45 minutes).
- Interview with the hiring manager (1 hour).
- Case study and presentation (1 hour).
- Team interaction session (1 hour or half-day immersion).
- Reference checks.
- Submission of a criminal record check.
Job Details
- Permanent position.
- Full-time role.
At Doctolib, we are committed to enhancing healthcare access for everyone, regardless of background or identity. We celebrate diversity and strive to create an inclusive environment. We welcome applications from all individuals, regardless of gender, religion, age, sexual orientation, ethnicity, disability, or origin.
