Refining Chemical Integration Engineer - Doha, دولة قطر - MatchaTalent

وصف

Coders Connect are partnering with Sanofi.

At Sanofi Consumer Healthcare, we have one shared mission we work passionately, every day, to serve healthier, fuller lives now and for the generations to come.

In order to do so, we strive to act as a force for good by integrating sustainability along our business and employees mission and operate responsibly from both a social and environmental point of view.

Who You Are:

You are a dynamic MLOps Engineer interested in challenging the status quo to ensure seamless MLOps that scale up Sanofis AI solutions for the patients of tomorrow. You are an influencer and leader who has deployed AI/ML solutions with technically robust lifecycle management (e.g., new releases, change management, monitoring and troubleshooting) and infrastructural support. You have a keen eye for improvement opportunities and a demonstrated ability to deliver using software engineering and MLOps skills while working across the full stack and moving fluidly between programming languages and technologies.

Our vision for digital, data analytics and AI

Join us on our journey in enabling Sanofi s Digital Transformation through becoming an AI first organization. This means:

• AI Factory - Versatile Teams Operating in Cross Functional Pods: Utilizing digital and data resources to develop AI products, bringing data management, AI and product development skills to products, programs and projects to create an agile, fulfilling and meaningful work environment.
  
• Leading Edge Tech Stack: Experience build products that will be deployed globally on a leading edge tech stack.
  
• World Class Mentorship and Training: Working with renown leaders and academics in machine learning to further develop your skillsets
  

Job Highlights:

• Work in agile pods to design and build cloud hosted, ML products with automated pipelines that run, monitor, and retrain ML Models
  
• Design AI/ML apps and implement automated model and pipeline adaption and validation working closely with data scientists and data engineers
  
• Support life cycle management of deployed ML apps (e.g., new releases, change management, monitoring and troubleshooting).
  
• Work as MLOps subject matter expert (e.g., develop and maintain enterprise standards, user guides, release notes, FAQs)
  
• Build processes supporting seamless MLOps (e.g., app monitoring, troubleshooting, life cycle management and customer support)
  
• Walk stakeholders and solution partners through solutions and reviewing product change and development needs.
  
• Maintain effective relationships with app userbase to develop education and communication content as per life cycle events
  
• Researching and gain expertise on emerging tools and technologies. An enthusiasm to ask questions and try and learn new things is essential.
  
• Strong personal interest in learning, researching, and creating new solutions with high customer impact
  

Requirements

Key Functional Requirements & Qualifications:

• Experience in data science, statistics, software engineering, modular design and design thinking.
  
• Experience developing CI/CD pipelines for AI/ML development, deploying models to production, and managing the lifecycle in a regulated environment.
  
• Experience building and deploying data science apps with large scale data and ML pipelines and architectures.
  
• Experience working in an agile pod supporting and working with cross-functional teams.
  
• Good understanding of ML and AI concepts and hands-on experience in development, deployment and agile life cycle management of data science apps (MLOps).
  
• Ability to assess new technologies and compile architecture decision records (ADRs).
  
• Excellent communication skills in English, both verbal and in writing.
  

Key Technical Requirements & Qualifications:

• Graduate degree in Computer Science, Information Systems, Software Engineering or another quantitative field
  
• Ability to work across the full stack and move fluidly between programming languages and MLOps technologies (e.g.: Python, Spark, R, DataBricks, Github, MLFlow, Airflow)
  
• Advanced Python is a MUST.
  
• Experience in cloud and high-performance computing environments (AWS and Databricks preferred)
  
• Experience in AWS (e.g.: S3, Lambda, EC2, cloud watch) and other similar technologies (e.g.: ELK stack, Snowflake, Informatica)
  
• Knowledge of SQL and relational databases, query authoring (SQL) and designing variety of databases (e.g., Postgres SQL).
  
• Experience with visualization technologies (e.g.: RShiny, Python DASH, Tableau, PowerBI)
  
• Experience in development, deployment and operations of AI/ML modelling of complex datasets
  
• Experience in developing and maintaining APIs (e.g.: REST, FastAPI)
  
• Experience specifying infrastructure and Infrastructure as a code (e.g.: docker, Kubernetes, Terraform)
  
• Experience in cloud-based ML engineering in an industrial setting within a global organization (technology company preferred)
  
• Experience on working within compliance (e.g.: quality, regulatory - data privacy, GxP, SOX) and cybersecurity requirements is a plus
  
• For more senior roles, mentoring and/or technology evangelism/advocacy experience
  

Key Functional Requirements & Qualifications: Experience in data science, statistics, software engineering, modular design and design thinking. Experience developing CI/CD pipelines for AI/ML development, deploying models to production, and managing the lifecycle in a regulated environment. Experience building and deploying data science apps with large scale data and ML pipelines and architectures. Experience working in an agile pod supporting and working with cross-functional teams. Good understanding of ML and AI concepts and hands-on experience in development, deployment and agile life cycle management of data science apps (MLOps). Ability to assess new technologies and compile architecture decision records (ADRs). Excellent communication skills in English, both verbal and in writing. Key Technical Requirements & Qualifications: Graduate degree in Computer Science, Information Systems, Software Engineering or another quantitative field Ability to work across the full stack and move fluidly between programming languages and MLOps technologies (e.g.: Python, Spark, R, DataBricks, Github, MLFlow, Airflow) Advanced Python is a MUST. Experience in cloud and high-performance computing environments (AWS and Databricks preferred) Experience in AWS (e.g.: S3, Lambda, EC2, cloud watch) and other similar technologies (e.g.: ELK stack, Snowflake, Informatica) Knowledge of SQL and relational databases, query authoring (SQL) and designing variety of databases (e.g., Postgres SQL). Experience with visualization technologies (e.g.: RShiny, Python DASH, Tableau, PowerBI) Experience in development, deployment and operations of AI/ML modelling of complex datasets Experience in developing and maintaining APIs (e.g.: REST, FastAPI) Experience specifying infrastructure and Infrastructure as a code (e.g.: docker, Kubernetes, Terraform) Experience in cloud-based ML engineering in an industrial setting within a global organization (technology company preferred) Experience on working within compliance (e.g.: quality, regulatory - data privacy, GxP, SOX) and cybersecurity requirements is a plus For more senior roles, mentoring and/or technology evangelism/advocacy experience