Infrastructure Solutions Architect with AI

About the role:

The Infrastructure Architect will have a strong background in designing, implementing, and scaling enterprise infrastructure with a focus on AI workloads and platforms. This role will be instrumental in shaping the technical foundation required to support AI/ML models, data pipelines, and high-performance compute environments across our organization. The role will combine expertise in cloud, networking, and storage architectures with knowledge of AI infrastructure needs such as GPU clusters, model training/deployment environments, and MLOps frameworks.

Responsibilities:

Key Responsibilities | Architecture & Strategy

• Design and implement infrastructure architectures to support enterprise AI workloads (training, inference, and data processing)
• Define scalable strategies for on-prem, cloud, or hybrid environments optimized for AI/ML performance
• Develop roadmaps for AI infrastructure adoption and integration into existing IT landscapes

Key Responsibilities | Infrastructure Engineering

• Architect GPU/accelerator-based compute clusters and storage solutions optimized for large-scale AI workloads
• Collaborate with data scientists and ML engineers to understand infrastructure requirements for model training and deployment
• Ensure high availability, scalability, and cost-efficiency of AI workloads.

Key Responsibilities | Cloud & DevOps

• Design cloud-native solutions leveraging services like AWS Sagemaker, Azure ML, or GCP Vertex AI
• Establish MLOps pipelines and CI/CD frameworks for AI/ML (gitlab ci/cd,etc.)
• Automate provisioning, monitoring, and scaling of AI infrastructure

Key Responsibilities | Governance and Security

• Define best practices for data governance, compliance, and security in AI systems
• Ensure responsible usage of AI infrastructure with strong observability and governance controls
• Optimize resource utilization and manage budgets for high-performance compute environments

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 5+ years of experience in infrastructure architecture, cloud solutions, or systems engineering
• Proven experience with AI/ML infrastructure (GPU clusters, distributed training, containerization, Kubernetes, etc.)
• Strong knowledge of cloud platforms (AWS, Azure, GCP) and their AI/ML services
• Experience with MLOps tools (Kubeflow, MLflow, Airflow, etc.)
• Solid understanding of networking, storage, and security principles
• Ability to communicate complex technical concepts to both technical and non-technical stakeholders
• Strong development / TL experience, preferably with python

Nice to Have

• Experience with HPC (High-Performance Computing) or large-scale distributed systems
• Hands-on experience with deep learning frameworks (PyTorch)
• Knowledge of data platforms (Hadoop, etc.)
• Familiarity with emerging generative AI infrastructure technologies (LLM hosting, vector databases, retrieval-augmented generation