Enterprise AI Solution Architect - Delhi
Rapyder Cloud Solutions- Posted 3 hours ago
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Job Description
Team: AI - Solutions Team (Pre-sales)
Location: Delhi|Experience: 3-5 Years|Joining: Immediate to 45 Days
About the RoleRapyder is building its AI Practice, and we are looking for an Enterprise AI Solution Architect who can translate complex customer business problems into compelling, outcome-driven AI solutions. This is a high-visibility, customer-facing role at the intersection of cloud infrastructure, AI/ML platforms, and enterprise pre-sales.
You will work alongside our Business Development and Tech Sales teams to architect solutions, lead technical discovery workshops, respond to RFPs, and build proof-of-concepts that demonstrate Rapyder's AI capabilities to mid-market and enterprise customers.
If you are passionate about AI-led transformation, enjoy whiteboarding with CXOs, and can bridge the gap between business outcomes and technical architecture - this role is for you.
Key Responsibilities•Solution Design & Architecture: Design end-to-end AI/ML and GenAI solutions on AWS and Azure, covering data pipelines, model training/fine-tuning, inference infrastructure, integration layers and GenAI/AgenticAI workflows.
•Pre-Sales Engagement: Lead technical discovery calls, solution workshops, and architecture review sessions with CTO/CIO/AI leads at enterprise accounts.
•Proposal & RFP Response: Own the technical sections of RFPs and solution proposals; create architecture diagrams, effort estimates, and solution narratives.
•Proof of Concept (PoC): Build and demonstrate PoCs that validate AI solution feasibility, using AWS SageMaker, Bedrock, Azure Foundry, Copilot or equivalent platforms.
•Customer Advisory: Act as a trusted AI advisor to customers - map their business goals to AI roadmaps, identify quick wins, and propose phased implementation plans.
•Cross-functional Collaboration: Work closely with the Delivery, Sales, and Tech Sales teams to ensure smooth transition from pre-sales to delivery; participate in handover reviews.
•Thought Leadership: Contribute to case studies, solution accelerators, internal knowledge bases, and represent Rapyder at customer events and webinars.
•Market Intelligence: Track AI platform updates (AWS, Azure, GCP, NVIDIA, open-source LLMs) and bring relevant innovations proactively to customer conversations.
What We're Looking For•3-5 years of experience in cloud solution architecture, AI/ML pre-sales, or enterprise technology consulting.
•Hands-on experience with AWS services (SageMaker, Bedrock, Lambda, Step Functions, S3, Glue, etc.,) or Azure AI/ML services (Azure Foundry, Azure ML, Data Factory, etc.,).
•Working knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and AI agent frameworks (LangChain, LlamaIndex, AutoGen, etc.).
•Ability to architect end-to-end data and AI pipelines - from ingestion and processing to model serving and monitoring.
•Strong communication skills - can present confidently to both technical architects and business executives.
•Experience in responding to RFPs and creating solution architecture documents.
•Familiarity with MLOps practices: model versioning, CI/CD for ML, monitoring and drift detection.
•Bachelor's degree in Computer Science, Engineering, or a related field; advanced degree is a plus.
Good to Have•AWS Certified Solutions Architect (Associate or Professional), AWS Certified Machine Learning Specialty, or equivalent Azure certifications.
•Exposure to EdgeAI, VisionAI, or Physical AI use cases in Manufacturing, Retail, or Logistics.
•Experience with NVIDIA platforms (NIM, NeMo, Jetson) or similar silicon-layer AI deployment tools.
•Knowledge of Sovereign Cloud requirements and data residency regulations
•Familiarity with open-source LLMs (Mistral, LLaMA, Falcon) and their fine-tuning / deployment pipelines.
•Understanding of enterprise data governance, security architecture, and compliance frameworks.
•Prior experience working in a cloud consulting or system integrator environment.
More Info
Key Skills
MLOps practices
Retrieval-Augmented Generation (RAG)
model versioning
monitoring and drift detection
CI/CD for ML
AI agent frameworks
Large Language Models (LLMs)
prompt engineering
end-to-end data and AI pipelines
