This is a remote position.

Job Summary:

The AI Solution Architect is responsible for designing and overseeing the implementation of AI solutions that align with business objectives and technology strategies. This role involves working closely with cross-functional teams, including data scientists, AI engineers, and business stakeholders, to ensure that AI technologies are integrated effectively and efficiently. The AI Solution Architect will provide technical leadership, define architecture standards, and ensure that AI systems are scalable, secure, and maintainable.

Key Responsibilities:

1. AI Solution Design:
   
   • Lead the design and architecture of AI solutions, ensuring alignment with business goals and technology strategies.
     
   • Develop end-to-end AI architectures that encompass data collection, model development, deployment, and integration with existing systems.
     
   • Collaborate with data scientists and engineers to define AI model requirements and ensure that they are incorporated into the overall architecture.
     
2. Technical Leadership:
   
   • Provide technical guidance and mentorship to AI development teams, ensuring best practices in AI/ML model development, data engineering, and system integration.
     
   • Stay updated on the latest AI technologies, tools, and frameworks, and incorporate them into the architecture when appropriate.
     
   • Lead technical discussions and decision-making processes related to AI solution design and implementation.
     
3. Stakeholder Collaboration:
   
   • Work closely with business stakeholders to understand their needs and translate them into AI-driven solutions.
     
   • Act as a liaison between business and technical teams, ensuring that AI solutions meet both functional and non-functional requirements.
     
   • Present AI solution designs and architecture to stakeholders, addressing concerns and incorporating feedback.
     
4. Scalability and Performance:
   
   • Design AI architectures that are scalable, ensuring that AI solutions can handle large volumes of data and complex computations.
     
   • Optimize AI systems for performance, ensuring that models and algorithms run efficiently in production environments.
     
   • Implement monitoring and performance tuning strategies to maintain system health and responsiveness.
     
5. Security and Compliance:
   
   • Ensure that AI solutions are designed with security in mind, incorporating best practices for data protection, privacy, and ethical AI usage.
     
   • Ensure compliance with relevant regulations and industry standards, particularly in sensitive areas such as healthcare, finance, and data privacy.
     
   • Conduct risk assessments and implement mitigation strategies to address potential vulnerabilities in AI systems.
     
6. Integration and Deployment:
   
   • Oversee the integration of AI solutions with existing systems, ensuring seamless interoperability and data flow.
     
   • Define deployment strategies, including CI/CD pipelines for AI models, ensuring smooth transitions from development to production environments.
     
   • Collaborate with DevOps teams to automate AI model deployment and monitor AI solutions in production.
     
7. Documentation and Knowledge Sharing:
   
   • Create detailed documentation for AI architectures, including design decisions, data flows, and system dependencies.
     
   • Share knowledge and best practices with the broader team, fostering a culture of continuous learning and improvement.
     
   • Conduct training sessions and workshops to educate stakeholders on AI technologies and their application.
     
8. Continuous Improvement:
   
   • Evaluate the effectiveness of AI solutions post-deployment, identifying areas for improvement and optimization.
     
   • Lead retrospectives and lessons learned sessions to capture feedback and improve future AI projects.
     
   • Drive innovation by exploring new AI use cases and potential applications within the organization.
     

Qualifications:

• Education:
  
  • Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field. A Master’s degree or relevant certifications (e.g., AWS Certified Machine Learning, Google Professional ML Engineer) is a plus.
    
• Experience:
  
  • Proven experience (7+ years) in software architecture and design, with at least 3 years focused on AI/ML solutions.
    
  • Strong background in AI/ML technologies, including experience with popular frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
    
  • Experience in designing and deploying AI solutions in production environments, preferably in cloud-based infrastructures (e.g., AWS, Azure, Google Cloud).
    
• Skills:
  
  • Strong understanding of AI/ML lifecycle, including data preprocessing, model training, validation, deployment, and monitoring.
    
  • Proficiency in data engineering and architecture, with experience in ETL processes, data pipelines
    
  • Solid knowledge of DevOps practices and tools, including CI/CD pipelines, containerization (e.g., Docker, Kubernetes), and version control (e.g., Git).
    
  • Excellent problem-solving skills, with the ability to address complex technical challenges.
    
  • Strong communication and interpersonal skills, with the ability to work effectively with both technical and non-technical stakeholders.
    

• Experience in AI ethics and governance, with a strong understanding of bias detection, fairness, and transparency in AI systems.
  
• Familiarity with natural language processing (NLP), computer vision, or reinforcement learning techniques.
  
• Knowledge of Agile methodologies and experience working in Agile development environments.