Senior Solutions Architect – AI
Mohr Talent
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Job summary
Mohr Talent is seeking a Senior Solutions Architect – AI to lead enterprise AI initiatives across the organization from Naperville, IL. The role drives AI strategy, architecture, and governance, guiding multiple Agile teams of data scientists and engineers to deliver scalable AI-powered solutions.
The ideal candidate has extensive AI/ML architecture experience, strong leadership, and a track record of delivering measurable business value through AI programs.
Qualifications
- Master’s degree or equivalent in CS/Data Science/AI or related field.
- 10+ years in AI/ML product development, solution architecture, data science, or related technical disciplines.
- 4+ years leading teams of Data Scientists, AI Engineers, ML Engineers, or Architects.
- Experience delivering enterprise AI initiatives (personalization, recommendations, CV, optimization).
- Hands-on with ML frameworks (PyTorch, TensorFlow or similar).
- Experience with cloud-based AI/ML services and full AI/ML lifecycle.
Responsibilities
- Lead AI product development strategies, roadmaps, and implementation standards across multiple AI initiatives.
- Provide technical leadership to Data Scientists, AI Engineers, ML Engineers, and Architects.
- Develop scalable AI architectures, model deployment, monitoring, and governance practices.
- Collaborate with product management to align AI priorities with business goals.
- Promote responsible AI practices and governance within projects.
- Mentor Agile teams and ensure high-quality, scalable AI solutions.
Skills
Education
Tools
Job description
The Senior Solutions Architect – AI will provide technical leadership for artificial intelligence and machine learning initiatives across the organization, directing multiple Agile teams focused on developing and deploying AI-powered solutions.
This individual will serve as a technical bridge between data science, engineering, enterprise architecture, shared services, product management, and business stakeholders. The Senior Solutions Architect will guide teams throughout the complete AI product lifecycle, from initial concept and proof of concept through production deployment, monitoring, and continuous improvement.
The ideal candidate brings extensive AI/ML architecture and product development experience, strong technical leadership capabilities, and a track record of delivering enterprise AI solutions that create measurable business value.
Lead the strategic direction of AI product development, including solution architecture, technical roadmaps, and implementation standards across multiple AI initiatives and platforms.
Maintain alignment with Principal Solution Architects, Enterprise Architecture, and supporting technology teams.
Guide the development of data pipelines, feature engineering processes, and model training workflows.
Establish and promote MLOps standards and best practices for model deployment, monitoring, maintenance, and continuous improvement.
Lead technical reviews focused on AI model performance, scalability, reliability, and business impact.
Establish metrics and KPIs to measure the effectiveness and value of AI initiatives.
Evaluate emerging AI technologies, methodologies, frameworks, and tools for potential enterprise adoption.
Develop AI solution approaches, model-selection strategies, system architectures, and deployment strategies.
Design and enable AI-powered solutions that enhance customer experiences and improve operational efficiency.
Support AI applications involving personalization, recommendation systems, inventory optimization, pricing, supply chain, and other enterprise operations.
Develop reusable AI components, frameworks, patterns, and services to accelerate future AI initiatives.
Develop proofs of concept (POCs) to validate potential AI applications before full-scale implementation.
Ensure AI architectures are scalable, secure, maintainable, and aligned with enterprise technology standards.
Provide technical direction and mentorship to multiple Agile teams consisting of Data Scientists, AI Engineers, Machine Learning Engineers, and Architects.
Foster a culture of technical excellence, innovation, collaboration, and continuous learning.
Guide teams through technical challenges involving model development, deployment, integration, and production support.
Promote AI-focused Agile development practices across delivery teams.
Serve as a key contributor within the Enterprise Architecture function.
Ensure AI initiatives comply with enterprise infrastructure, security, architecture, and data governance standards.
Collaborate with Enterprise Architects to establish and promote AI architecture principles, standards, and reusable patterns.
Influence the evolution of enterprise technology standards as AI capabilities continue to expand.
Participate in Architecture Review Boards and provide technical leadership for proposed AI initiatives.
Translate complex business requirements and challenges into scalable AI solution strategies and technical roadmaps.
Partner with AI Product Managers and business stakeholders to align AI initiatives with organizational priorities.
Develop measurement frameworks to evaluate the operational and financial impact of AI solutions.
Identify opportunities where AI and machine learning can improve customer experience, productivity, decision-making, and operational efficiency.
Ensure AI solutions comply with applicable privacy, security, governance, and ethical standards.
Partner with Responsible AI Governance teams to promote transparency, explainability, accountability, and ethical AI usage.
Provide thought leadership regarding AI architecture, governance, implementation, and emerging best practices.
Help establish standards for responsible development and deployment of enterprise AI solutions.
Partner with Product Management to identify and prioritize opportunities for AI implementation.
Build consensus among technical and business stakeholders regarding AI priorities and investment decisions.
Partner with Product Owners to translate business requirements into AI-specific technical requirements.
Communicate complex AI and machine learning concepts effectively to both technical and non-technical audiences.
- Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field; equivalent professional experience will also be considered.
- 10+ years of experience in AI/ML product development, solution architecture, data science, or related technical disciplines.
- 4+ years of experience leading teams of Data Scientists, AI Engineers, Machine Learning Engineers, or Architects.
- Demonstrated success leading enterprise AI initiatives involving areas such as:
- Personalization
- Recommendation systems
- Computer vision
- Operational efficiency and optimization
- In-depth understanding of machine learning algorithms, architectures, frameworks, and development best practices.
- Hands‑on experience with modern machine learning frameworks such as PyTorch, TensorFlow, or similar technologies.
- Experience working with cloud-based AI and machine learning services.
- Strong experience managing the full AI/ML product lifecycle, from initial concept and POC through production deployment and ongoing optimization.
- Experience implementing MLOps practices and tools for model deployment, monitoring, maintenance, and governance.
- Proven track record of implementing AI solutions that delivered measurable business value.
- Demonstrated ability to translate complex business challenges into practical AI/ML solution approaches.
- Experience working within Agile AI/ML development environments.
- Strong understanding of data pipelines, feature engineering, model training, deployment, and monitoring.
- Excellent communication and stakeholder-management skills with the ability to explain complex AI concepts to technical and non-technical audiences.
- Familiarity with AI governance, ethics, privacy, security, and Responsible AI principles.
- Ability to effectively work within an onshore/offshore development model.
- Experience within retail, e-commerce, consumer products, or other large-scale customer-facing environments.
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