AIML Architect
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Job Location:
Jefferson County, NY - USA
Monthly Salary:
Not Disclosed
Posted on:
1 hour ago
Vacancies:
1 Vacancy
Job Summary
Job role: AI/ML Architect
Location: Philadelphia (Onsite)
Description:
Seeking a senior AI/ML platform leader to design an operationalize a scalable production ready AI/ML architecture across multiple business units.
This role is responsible for moving the organization from proof of concept AI efforts to repeatable governed delivery of models in production.
The ideal candidate has built and deployed machine learning systems at scale understands both ML development and platform engineering and can define the environments pipelines and architectural standards that enable team to ship AI safely and efficiently.
This is a hands-on architectural leadership role with significant influence across product engineering data and security teams.
Key responsibilities:
Include AIML architecture and platform design define the end-to-end AI/ML reference architecture from Data injection through model serving and monitoring. Establish standards for data storage access patterns and lineage including separation of raw curated and feature ready data. Assess and define the need for shared platform capabilities such as
feature stores
model registries
AIML catalogues
experiment tracking
design for scale across multiple business units with differing data sensitivity regulatory and operational needs
Environment and delivery pipeline: Define standard development validation and production environments for AIML workloads. Designer a repeatable ML delivery pipeline covering
model development and training
validation approval and promotion
deployment (batch and/or real time)
monitoring drift detection and retraining
establish CI/CD (and continuous training where appropriate) best practices for ML systems
MLOps governance and production readiness: Define what production read means for AIML models including:
testing and validation requirements
monitoring and alerting
rollback and incident response patterns
partner with security legal and compliance team to integrate governance without slowing delivery
ensure models are discoverable auditable and traceable across environment and business units
Enablement and operating model: Create a paved road for AI development
shared standards templates and tooling that business unit can serve against
advise and enable product teams and engineering teams moving models from POC to production
help define the long-term operating model for AI/ML ownership across central platform teams and federated BU teams
Required qualifications:
10 years of experience in software engineering data platforms or ML engineering
5 years of hands-on experience deploying machine learning systems into production
proving experience indesigning AI/ML platform or MLOps architectures(not just individual models)
strong understanding of:
ML life cycle management data
Data pipelines and feature engineering
model serving patterns (batch real time APIs )
experience working across organizational boundaries (multiple business teams and units)
Ability to communicate architectural designs and decisions clearly to both technical and non-technical stakeholders
Preferred qualification: Experience designing or operating
features stores
model registries and experiment tracking platforms
AI governance and risk framework
familiarity with cloud native ML platforms and infrastructure (AWS GCP Azure or similar)
Experience monitoring teams and establishing standards at scale
background in regulated or data sensitive environment
What does success looks like in 12 to 18 months:
A documented adopted AIML reference architecture used cross business units
Clear standardize environment and pipelines enabling faster promotion from POC to production
Reduced the duplication of feature engine engineering and model deployment efforts
consistent visibility into which models are running in production and how they perform performing
Increase confidence from leadership and organizations ability to deliver AI responsibly and scale
Location: Philadelphia (Onsite)
Description:
Seeking a senior AI/ML platform leader to design an operationalize a scalable production ready AI/ML architecture across multiple business units.
This role is responsible for moving the organization from proof of concept AI efforts to repeatable governed delivery of models in production.
The ideal candidate has built and deployed machine learning systems at scale understands both ML development and platform engineering and can define the environments pipelines and architectural standards that enable team to ship AI safely and efficiently.
This is a hands-on architectural leadership role with significant influence across product engineering data and security teams.
Key responsibilities:
Include AIML architecture and platform design define the end-to-end AI/ML reference architecture from Data injection through model serving and monitoring. Establish standards for data storage access patterns and lineage including separation of raw curated and feature ready data. Assess and define the need for shared platform capabilities such as
feature stores
model registries
AIML catalogues
experiment tracking
design for scale across multiple business units with differing data sensitivity regulatory and operational needs
Environment and delivery pipeline: Define standard development validation and production environments for AIML workloads. Designer a repeatable ML delivery pipeline covering
model development and training
validation approval and promotion
deployment (batch and/or real time)
monitoring drift detection and retraining
establish CI/CD (and continuous training where appropriate) best practices for ML systems
MLOps governance and production readiness: Define what production read means for AIML models including:
testing and validation requirements
monitoring and alerting
rollback and incident response patterns
partner with security legal and compliance team to integrate governance without slowing delivery
ensure models are discoverable auditable and traceable across environment and business units
Enablement and operating model: Create a paved road for AI development
shared standards templates and tooling that business unit can serve against
advise and enable product teams and engineering teams moving models from POC to production
help define the long-term operating model for AI/ML ownership across central platform teams and federated BU teams
Required qualifications:
10 years of experience in software engineering data platforms or ML engineering
5 years of hands-on experience deploying machine learning systems into production
proving experience indesigning AI/ML platform or MLOps architectures(not just individual models)
strong understanding of:
ML life cycle management data
Data pipelines and feature engineering
model serving patterns (batch real time APIs )
experience working across organizational boundaries (multiple business teams and units)
Ability to communicate architectural designs and decisions clearly to both technical and non-technical stakeholders
Preferred qualification: Experience designing or operating
features stores
model registries and experiment tracking platforms
AI governance and risk framework
familiarity with cloud native ML platforms and infrastructure (AWS GCP Azure or similar)
Experience monitoring teams and establishing standards at scale
background in regulated or data sensitive environment
What does success looks like in 12 to 18 months:
A documented adopted AIML reference architecture used cross business units
Clear standardize environment and pipelines enabling faster promotion from POC to production
Reduced the duplication of feature engine engineering and model deployment efforts
consistent visibility into which models are running in production and how they perform performing
Increase confidence from leadership and organizations ability to deliver AI responsibly and scale
Required Skills:
AIMLAWSAZURE
Key Skills
- APIs
- Pegasystems
- Spring
- SOAP
- .NET
- Hybris
- Solution Architecture
- Service-Oriented Architecture
- Adobe Experience Manager
- J2EE
- Java
- Oracle
