AI Solutions Architect

Position Summary

The AI Solutions Architect is responsible for designing, implementing, and guiding enterprise AI solutions that align with business objectives. This role bridges the gap between business strategy, AI/ML technology, and IT infrastructure, ensuring that AI initiatives are scalable, secure, and operationally effective. The AI Solutions Architect collaborates with data scientists, ML engineers, software developers, and business stakeholders to deliver end-to-end AI solutions.

Key Responsibilities

Solution Design & Architecture

• Design scalable, reliable, and secure AI/ML solutions aligned with organizational goals.
• Develop architectural blueprints for AI applications, including data pipelines, model deployment, and integration with existing IT systems.
• Evaluate and select AI technologies, platforms, frameworks, and cloud services appropriate for each solution.

Collaboration & Stakeholder Management

• Work with business stakeholders to understand requirements, define KPIs, and identify AI opportunities.
• Collaborate with data scientists, ML engineers, software developers, and DevOps teams to ensure successful implementation of AI solutions.
• Translate complex technical concepts into clear recommendations for non-technical stakeholders.

Technical Leadership

• Provide guidance on AI model selection, development best practices, and integration strategies.
• Ensure AI solutions comply with security, privacy, and regulatory requirements.
• Mentor technical teams on AI architecture patterns, tools, and emerging technologies.

Implementation Oversight

• Oversee end-to-end deployment of AI models and applications, including data ingestion, preprocessing, training, and inference.
• Collaborate with DevOps and IT teams to establish CI/CD pipelines and MLOps practices for AI solutions.
• Monitor solution performance and recommend optimizations for efficiency, scalability, and accuracy.

Research & Continuous Improvement

• Stay current on AI/ML trends, emerging frameworks, and best practices.
• Evaluate new AI technologies and tools to enhance solution effectiveness and innovation.
• Develop proof-of-concepts (POCs) to test new AI strategies and methodologies.

Qualifications

Required

• 5–10+ years of experience in AI/ML development, data science, or AI solution design.
• Strong experience in AI/ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) and cloud AI services (AWS SageMaker, Azure ML, GCP AI Platform).
• Deep understanding of AI architecture, model deployment, and integration patterns.
• Experience designing data pipelines, APIs, and scalable systems for AI applications.
• Excellent problem-solving, analytical, and communication skills.

Preferred

• Experience with MLOps, CI/CD pipelines, and containerization (Docker, Kubernetes).
• Knowledge of NLP, computer vision, or predictive analytics solutions.
• Understanding of security, privacy, and compliance considerations in AI solutions.
• Experience leading cross-functional AI projects or technical teams.