Senior Presales Architect

Strategic Technical Discovery

• →Lead deep technical discovery with engineering teams and technical founders
• →Understand model requirements, traffic expectations, latency constraints, GPU economics, and system dependencies
• →Translate customer ambition into production-feasible architectures
• →Identify hidden technical risks early

Commercial Acceleration

• →Partner tightly with Sales on strategic deals
• →Influence deal strategy through architectural clarity
• →Prevent misaligned commitments before engineering allocation
• →Increase PoC-to-production conversion by ensuring technical realism

PoC Architecture & Validation

• →Define measurable success criteria (latency, TTFT, throughput, cost envelope)
• →Classify workload complexity and required optimization depth
• →Align appropriate resources (ML Solution Architects, engineering, GPU capacity, etc.)
• →Drive structured Go / No-Go decisions
• →Prevent uncontrolled customization or hidden R&D

Pattern Recognition & Platform Leverage

• →Identify recurring configuration patterns across customers
• →Quantify demand for advanced optimizations (quantization, speculative decoding, etc.)
• →Surface structured insights to Product and Engineering
• →Help evolve platform capabilities based on real workload data

• Deep understanding of AI inference systems and GPU-backed infrastructure
• Experience with LLM workloads and performance-sensitive environments
• Experience with inference frameworks and libraries (e.g., vLLM, SGLang, TensorRT-LLM).
• Ability to reason about latency, throughput, cost, and architecture tradeoffs
• Strong customer presence with engineering-first organizations
• Comfort challenging assumptions and pushing back constructively
• Commercial awareness – you understand that engineering time is a strategic resource

• Programming Languages– Python
• Frameworks and Libraries– vLLM, SGLang, TensorRT-LLM, OpenAI/Anthropic SDKs
• Frameworks for Agentic Pipelines : Langchain / Langsmith / smolagents / equivalent
• API and Web Frameworks– FastAPI, Flask
• MLOps and DevOps tools– Kubernetes (K8s), Docker, Git
• Cloud Platforms– AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)

• Strategic deals are technically sound before engineering engagement
• PoCs are clearly scoped and economically justified
• Engineering capacity is allocated predictably
• Conversion to production improves
• Customers view you as a trusted architectural advisor