Solutions Architect

Role overview

We are hiring a Mid/Senior Solutions Architect in Qatar to join a pre-sales function and help translate customer needs into practical, scalable, and resilient AI-driven solutions.

What you will do

• Work closely with prospective customers and partners in a consultative, customer-facing pre-sales environment.
• Prepare technical responses for proposals, tenders, and RFPs.
• Deliver product demonstrations independently and tailor them to both technical and non-technical audiences.
• Design AI/ML solution architectures across cloud, on-premise, sovereign, and hybrid environments.
• Advise on deployment, orchestration, monitoring, and CI/CD practices for ML operations.
• Support infrastructure planning for LLM workloads, including GPU sizing and performance evaluation.
• Contribute code in Python and SQL when needed for solution design or validation.

Must-have expertise

• Background in solutions architecture, sales engineering, implementation consulting, or a similar partner-facing technical role.
• Strong verbal and written communication skills with the ability to present to varied stakeholders.
• Working knowledge of AWS, Azure, and GCP, along with services such as SageMaker, Vertex AI, and AzureML.
• Practical experience with MLOps tools and workflows, including CI/CD, monitoring, Kubeflow, Flyte, and MLflow.
• Comfort using Docker and Kubernetes to package and deploy AI workloads.
• Understanding of LLM inference stacks such as vLLM, llama.cpp, and OpenVINO, plus formats like ONNX, .safetensors, and HuggingFace model hub assets.
• Experience estimating GPU needs for inference or training, from A10 through H200 class hardware.
• Ability to assess LLM quality and performance using metrics such as accuracy, latency, and throughput.
• Hands-on familiarity with Python, SQL, PyTorch, TensorFlow, and Hugging Face libraries/frameworks.

Preferred exposure

• Computer vision, speech, and vision-language model experience.
• Model optimization, quantization, or deployment on edge devices.
• Designing retrieval-augmented generation pipelines or multi-agent systems.
• Building batch and streaming data architectures and working with big data platforms.
• Awareness of data privacy, responsible AI, GDPR, and relevant national AI regulations.