AI Solution Architect

AI Solution Architect page is loaded## AI Solution Architect locations:
Westbrook, MEtime type:
Full time posted on:
Heute ausgeschriebenjob requisition :
J-051116

We are looking for an
** AI Solution Architect
** who will design and govern the architecture of complex solutions across multiple streams, ensuring consistency, scalability, and alignment with organizational objectives. This role requires a holistic view of all system components, bridging tools, pipelines, and processes to deliver integrated solutions. The ideal candidate will combine technical expertise in AI and model architecture with strong leadership and negotiation skills to align stakeholders toward a common vision.

This position blends responsibilities of a Technical Product Owner (TPO) and Delivery Solution Architect (DSA) to ensure both strategic direction and successful delivery.
** This is a hybrid role and will require you to be in the office 2 days per week.
** This role combines strategic vision and delivery execution, ensuring architectural consistency across data workflows, pipelines, and model operations. Key responsibilities include overseeing the AI lifecycle—from data preparation to model deployment—while defining standards for model architecture, performance optimization, and interoperability. The position requires expertise in advanced AI techniques, MLOps practices, and cloud-based platforms, along with strong leadership and stakeholder management skills.

Ideal candidates will have experience in solution architecture for complex AI systems, proficiency in frameworks like Tensor Flow or PyTorch, and the ability to align multiple teams toward a unified vision.
** In this Role You will:**
* ** End-to-End Delivery**:
Drive solution delivery across all streams, ensuring timely and high-quality outcomes.
* ** Holistic System Oversight**:
Maintain visibility and attentiveness to all components within the solution ecosystem, including data ingestion, annotation tools, processing pipelines, and integration points.
* ** Cross-Team Consistency**:
Ensure architectural consistency across all technical leads and solution components.
* ** Integration Leadership**:
Act as the architectural bridge between annotation tools and training pipelines, enabling smooth interoperability and data flow.
* ** Vision Alignment**:
Collaborate with technical leads and stakeholders to establish and maintain a unified architectural vision.
* ** Constraint Enforcement**:
Define and enforce architectural standards, constraints, and best practices to ensure compliance, scalability, and maintainability.
* ** Solution Design**:
Develop scalable, secure, and efficient solutions that meet business and technical requirements.
* ** AI Lifecycle Expertise**:
Understand and optimize the AI lifecycle, including data preparation, feature engineering, model training, evaluation, deployment, and inference processes.
* ** Model Architecture Leadership:
** Design and govern model architectures for machine learning and deep learning, including transformer-based models, multimodal architectures, and ensemble strategies.
* ** Performance Optimization:
** Implement strategies for model efficiency, scalability, and interpretability, leveraging techniques such as quantization, pruning, and distributed training.
* ** Platform Knowledge**:
Leverage data science platforms such as Databricks for data engineering, model development, and pipeline orchestration.
* ** Stakeholder Negotiation**:
Facilitate responsibility negotiation among internal and external stakeholders to ensure clarity and accountability.
* ** Hybrid Role**:
Combine responsibilities of TPO (strategic product vision and prioritization) and DSA (delivery-focused architecture and execution).
* Bachelor’s or Master’s degree in Computer Science, Information Systems, or related field.
* Proven experience in solution architecture design and implementation for complex systems.
* Strong understanding of AI lifecycle and model architecture principles (e.g., CNNs, RNNs, attention mechanisms, transformer-based models).
* Experience with data science platforms such as Databricks, and familiarity with MLOps practices.
* Knowledge of data…