AI Architect

Overview

Hybrid
Depends on Experience
Full Time

Skills

Artificial Intelligence
Cloud Computing
Deep Learning
Machine Learning (ML)
Machine Learning Operations (ML Ops)
architect

Job Details

We are excited to share with you the job description for the AI Architect at Global Applications. Based on your background and experience, we believe this could be a great opportunity for you.

The Role:

Job Title : AI Architect

Location : NYC NY

Duration : Full Time /Direct Hire

An AI Architect is a high-level professional who designs, builds, and oversees the implementation of an organization's AI infrastructure and strategy. They act as a bridge between business goals and the technical reality of AI systems, ensuring solutions are scalable, secure, and aligned with company objectives.

An AI Architect's job description typically includes a mix of technical expertise and strategic responsibilities.

Key Responsibilities

  • Designing AI solutions: They develop the blueprint for AI systems, including data architectures, pipelines, and the selection of appropriate algorithms, models, and frameworks. This involves a deep understanding of machine learning, deep learning, and natural language processing (NLP).
  • Strategic alignment: AI Architects work closely with business stakeholders to identify how AI can solve business problems and drive digital transformation. They translate business needs into technical requirements and ensure AI initiatives deliver measurable value.
  • Infrastructure planning: They select the right technologies and platforms (e.g., cloud, on-premises, or hybrid deployments) and ensure they integrate seamlessly with existing systems. This often requires expertise in cloud platforms like AWS, Azure, and Google Cloud.
  • Risk and ethical management: They work with security and risk teams to mitigate risks such as data privacy issues, model bias, and adversarial attacks. They are responsible for ensuring the ethical and responsible implementation of AI.
  • Team collaboration and leadership: They collaborate with and often lead cross-functional teams, including data scientists, data engineers, and software developers, to bring AI projects from concept to production. They must have strong communication and leadership skills to effectively guide these teams and explain complex concepts to non-technical stakeholders.

Required Skills & Qualifications

  • Technical Skills:
    • Deep knowledge of AI/ML frameworks: Proficiency with tools like TensorFlow, PyTorch, and scikit-learn.
    • Programming languages: Expertise in languages such as Python, R, and Java.
    • Data management: Strong skills in data processing, cleansing, and pipeline design.
    • Cloud computing: Experience with major cloud platforms (AWS, Azure, Google Cloud Platform) and containerization technologies like Docker and Kubernetes.
    • Software engineering & DevOps: An understanding of DevOps and MLOps principles for deploying and managing AI models in production.
  • Soft Skills:
    • Strategic thinking and problem-solving: The ability to analyze complex business challenges and design innovative, long-term AI solutions.
    • Communication: Excellent communication skills to bridge the gap between technical teams and business leaders.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.