Client is looking for a technical profile with a strong component of communication (simplification) and governance not strictly an AI developer. The candidate must be hands-on with the platform (Anaconda Enterprise Posit AWS SageMaker MLflow) and able to present AI capabilities to an executive audience.
This dual mandateplatform operations and governance implementation across all business unitsis what distinguishes this role from a purely technical one.
Key recurring terms in the description highlight the true priorities: ecosystem governance all business units executive stakeholders. The client is looking for someone who sees the AI platform as a shared service to evolve and structure not as a one-time project.
Experience in setting up an AI ecosystem (not just using AI tools) is the central priority.
Note: The client is likely targeting someone who has already operated an AI platform in a multi-team environment. A candidate familiar with cross-functional data environments will have a practical advantage for transversal governance.
Profiles to exclude:
AI developers focused on modeling without platform.
Cloud architects without an MLOps foundation.
AI transformation consultants without hands-on technical experience with clients listed tools
Requirements
Experience implementing an AI ecosystem. (Must have actually implemented not just used)
Strong knowledge of AI concepts including executive-level communication. (Simplification is explicit)
Mastery of software engineering best practices.
Strong data engineering knowledge.
Proficiency in Python and/or R. (Either is sufficient)
Proficiency in AWS SageMaker or Azure AI. (Either is sufficient)
Proficiency in Anaconda Enterprise or Posit (Either is sufficient)
Proficiency in MLflow or Airflow (Either is sufficient)
Special projects: POCs architecture validations complex initiatives (explicitly required)
WHAT WE ARE LOOKING FOR
A profile such as an AI platform administrator or manager with at least 5 years of experience who has already implemented or operated an AI ecosystem in production in a multi-team or cross-functional context.
The candidate must:
Master at least one tool from each category:
SageMaker or Azure AI
Anaconda Enterprise or Posit
MLflow or Airflow
Python or R
Act as a technical reference for clients AI teams
Establish governance rules across all business units
Present AI capabilities to executive audiences
The role also includes special initiatives: proof of concepts architecture validations complex projects.
Knowledge of data engineering (Snowflake AWS S3) and DevOps (GitHub Jenkins Kubernetes) is desirable. Experience in insurance or financial services is an asset but not required.
WHAT WE DO NOT WANT
Profiles focused on modeling/data science without AI platform operations experience
Less than 5 years of experience in AI and software engineering
No hands-on experience with at least one of clients named tools
No experience building shared or multi-team AI ecosystems
Inability to simplify and present AI concepts to executive stakeholders
Client is looking for a technical profile with a strong component of communication (simplification) and governance not strictly an AI developer. The candidate must be hands-on with the platform (Anaconda Enterprise Posit AWS SageMaker MLflow) and able to present AI capabilities to an executive audie...
Client is looking for a technical profile with a strong component of communication (simplification) and governance not strictly an AI developer. The candidate must be hands-on with the platform (Anaconda Enterprise Posit AWS SageMaker MLflow) and able to present AI capabilities to an executive audience.
This dual mandateplatform operations and governance implementation across all business unitsis what distinguishes this role from a purely technical one.
Key recurring terms in the description highlight the true priorities: ecosystem governance all business units executive stakeholders. The client is looking for someone who sees the AI platform as a shared service to evolve and structure not as a one-time project.
Experience in setting up an AI ecosystem (not just using AI tools) is the central priority.
Note: The client is likely targeting someone who has already operated an AI platform in a multi-team environment. A candidate familiar with cross-functional data environments will have a practical advantage for transversal governance.
Profiles to exclude:
AI developers focused on modeling without platform.
Cloud architects without an MLOps foundation.
AI transformation consultants without hands-on technical experience with clients listed tools
Requirements
Experience implementing an AI ecosystem. (Must have actually implemented not just used)
Strong knowledge of AI concepts including executive-level communication. (Simplification is explicit)
Mastery of software engineering best practices.
Strong data engineering knowledge.
Proficiency in Python and/or R. (Either is sufficient)
Proficiency in AWS SageMaker or Azure AI. (Either is sufficient)
Proficiency in Anaconda Enterprise or Posit (Either is sufficient)
Proficiency in MLflow or Airflow (Either is sufficient)
Special projects: POCs architecture validations complex initiatives (explicitly required)
WHAT WE ARE LOOKING FOR
A profile such as an AI platform administrator or manager with at least 5 years of experience who has already implemented or operated an AI ecosystem in production in a multi-team or cross-functional context.
The candidate must:
Master at least one tool from each category:
SageMaker or Azure AI
Anaconda Enterprise or Posit
MLflow or Airflow
Python or R
Act as a technical reference for clients AI teams
Establish governance rules across all business units
Present AI capabilities to executive audiences
The role also includes special initiatives: proof of concepts architecture validations complex projects.
Knowledge of data engineering (Snowflake AWS S3) and DevOps (GitHub Jenkins Kubernetes) is desirable. Experience in insurance or financial services is an asset but not required.
WHAT WE DO NOT WANT
Profiles focused on modeling/data science without AI platform operations experience
Less than 5 years of experience in AI and software engineering
No hands-on experience with at least one of clients named tools
No experience building shared or multi-team AI ecosystems
Inability to simplify and present AI concepts to executive stakeholders