Solutions Architect

Accountabilities:

• Serve as a trusted technical advisor to customers by providing expert guidance on cloud infrastructure, AI, and MLOps solutions throughout the customer lifecycle.


• Lead technical engagements including proof-of-concepts, workshops, training sessions, presentations, and architecture reviews to promote best practices and solution adoption.


• Collaborate with customers to understand business and technical requirements, translating them into scalable and efficient cloud architectures.


• Design, document, and implement Infrastructure-as-Code solutions while creating technical documentation and operational guidelines in collaboration with internal teams.


• Support customers in optimizing the performance, scalability, and resource utilization of AI and machine learning workloads.


• Act as the primary technical point of expertise for customer use cases, partnering with product, support, and marketing teams to drive successful outcomes.


• Contribute to technical marketing initiatives, including conferences, hackathons, webinars, workshops, and industry events.

Requirements

• 5+ years of experience in cloud architecture, system engineering, network engineering, software development, or a related technical field focused on cloud technologies.


• Strong hands-on expertise with Infrastructure-as-Code and configuration management tools, particularly Terraform and/or Ansible.


• Solid experience working with Kubernetes and proficiency in Python programming.


• Deep understanding of GPU-based computing environments for machine learning training and inference workloads.


• Knowledge of GPU software ecosystems, including drivers, CUDA, OpenCL, and related technologies.


• Excellent communication and presentation skills, with the ability to engage both technical and non-technical stakeholders.


• Customer-focused mindset with strong problem-solving and relationship-building capabilities.


• Experience with HPC or ML orchestration platforms such as Slurm or Kubeflow is a plus.


• Familiarity with deep learning frameworks including TensorFlow and PyTorch is advantageous.


• Knowledge of cloud-based machine learning tools and platforms from leading providers such as NVIDIA, AWS, Microsoft Azure, and Google Cloud is beneficial.

Benefits

• Competitive compensation package.


• Strong career development and continuous learning opportunities.


• Flexible work arrangements that support work-life balance.


• Collaborative, innovative, and high-performing work environment.


• Opportunity to contribute to impactful AI and cloud technology projects.


• Exposure to international teams and diverse technical expertise.


• High level of autonomy, ownership, and influence in shaping technical solutions.


• Dynamic environment that encourages professional growth and innovation.

This listing is from ats_lever. View original listing ↗