AI Solutions Architect

We are seeking an AI Solutions Architect to design and deliver end-to-end AI solutions that solve real customer and business problems. This role partners with engineering, FAE, marketing and sales teams to translate requirements into scalable architectures, validate them through prototypes, and drive successful production deployments across cloud and/or edge environments.

Responsibilities:

• Provide technical leadership across cross-functional teams; review designs, unblock engineering, and ensure solution quality through architecture and code reviews.

• Analyze AL/ML models and provide guidance on how to implement them on the FPGA, requiring detailed model analysis, and optimization for FPGA

• Design reference architectures for AI/ML systems, including data ingestion, feature pipelines, model inferencing, evaluation, serving, benchmarking, and continuous improvement.

• Architect and implement Generative AI solutions (when applicable) such as RAG, agentic workflows, prompt engineering patterns, guardrails, and evaluation frameworks, on FPGA platforms.

• Select appropriate software framework and justify trade-offs throughput, latency, and manageability.

• Build prototypes and proofs-of-concept (PoCs) to validate feasibility, performance, and value; turn successful PoCs into production-grade designs.

• Create high-quality technical documentation (architecture diagrams, application and user guide) and present solutions to technical and executive stakeholders.

• Engage with customers and partners in pre-sales and delivery contexts; deliver demos, technical deep-dives, and solution proposals.

• BS/MS in Computer Science, Computer Engineering, Data Science, or equivalent practical experience. PhD. preferred

• 15+ years of experience in AI/ML engineering, or solutions architecture

• Hands-on experience delivering ML solutions end-to-end (data pipelines, model serving, monitoring, benchmarking) with measurable business impact.

• Strong programming skills in Python and at least one additional language (e.g., C++); ability to read and review production code.

• Experience with modern ML frameworks and tooling (e.g., PyTorch, TensorFlow, vLLM, llama.cpp).

• Solid understanding of generative AI models such as Deepseek, Qwen, GPT and other models and basic statistics/ML concepts (bias/variance, evaluation metrics, overfitting, calibration).

• Experience with cloud architecture (AWS, Azure, or GCP)

Travel: As needed (typically up to 25%).