iLink Digital

We are seeking a visionary AI Architect to lead the design, development, and implementation of cutting-edge AI solutions. The ideal candidate will have a strong background in machine learning, data engineering, cloud architecture, and enterprise-grade solution design. You will collaborate with stakeholders to translate business challenges into scalable AI-driven systems.

Key Responsibilities :

- Lead the design and architecture of AI/ML solutions across the organization.

- Collaborate with data scientists, engineers, product owners, and business leaders to define AI use cases and deliverables.

- Define architecture standards and best practices for AI solution development and deployment.

- Ensure AI models are scalable, robust, and integrated with existing systems and data platforms.

- Oversee end-to-end MLOps processes, including versioning, monitoring, and continuous integration/deployment (CI/CD).

- Evaluate and select AI tools, frameworks, and technologies aligned with enterprise needs.

- Stay current on AI/ML trends, tools, and regulations (including ethical AI practices).

- Provide technical leadership and mentorship to development teams.

- Prepare architecture documentation and presentations for stakeholders and leadership.

Required Qualifications :

- Bachelor's or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related field.

- Proven experience in architecting AI/ML solutions in cloud environments (AWS, Azure, or GCP).

- Deep understanding of machine learning algorithms, NLP, computer vision, and deep learning frameworks (e.g., TensorFlow, PyTorch, Hugging Face).

- Strong proficiency in Python and relevant ML libraries.

- Solid experience with data engineering pipelines, data lakes, and APIs.

- Familiarity with MLOps platforms (e.g., MLflow, Kubeflow, SageMaker, Azure ML).

- Strong knowledge of software architecture patterns and microservices.

- Excellent communication, stakeholder management, and documentation skills.

Preferred Qualifications :

- AI/ML certification (e.g., Google Professional ML Engineer, AWS Machine Learning Specialty).

- Experience working in regulated industries (healthcare, finance, etc.).

- Familiarity with Generative AI, LLMs (like GPT), and Responsible AI principles.

- Experience in DevOps or Infrastructure as Code (e.g., Terraform, Docker, Kubernetes).