Enterprise Sr. AI Solution Architect

Team: AI - Solutions Team (Pre-sales)

Location: Bangalore   |   Experience: 5+ Years   |   Joining: Immediate to 45 Days

About the Role

Rapyder is scaling its AI Practice, and we are looking for an Enterprise Sr. AI Solution Architect who can turn complex customer business problems into outcome-driven AI solutions - and who has also seen those solutions through delivery. This is a high-visibility, customer-facing role at the intersection of cloud infrastructure, AI/ML platforms, enterprise pre-sales and delivery.

You will partner with our Business Development, Tech Sales and Delivery teams to shape and win large AI opportunities: leading technical discovery, owning solution architecture and RFP responses, building PoCs, and setting up delivery for success. You will also mentor Solution Architects and act as a senior technical voice with CTO/CIO/CDO stakeholders at mid-market and enterprise customers.

If you are passionate about AI-led transformation, enjoy whiteboarding with CXOs, and have the delivery scars that make your architectures and estimates credible - this role is for you.

Key Responsibilities

•         Solution Design & Architecture: Architect end-to-end AI/ML, GenAI and Agentic AI solutions on AWS and Azure (GCP a plus), covering data pipelines, model training/fine-tuning, RAG and agent workflows, inference infrastructure, integration layers, security, observability and cost (FinOps).

•         Pre-Sales Leadership: Lead technical discovery, solution workshops and architecture review sessions with CTO/CIO/CDO and AI leads; qualify opportunities, shape win themes and own the technical strategy for strategic deals.

•         Proposal, RFP & Commercials: Own the technical response for RFPs and proposals - architecture documents, delivery approach, realistic effort and team estimates, assumptions, risks and SOW scoping.

•         Proof of Concept & Pilots: Define success criteria and build/lead PoCs and pilots that validate feasibility and business value, using AWS SageMaker and Bedrock, Azure AI Foundry, Copilot or equivalent platforms; guide the path from PoC to production.

•         Delivery Assurance & Handover: Bring delivery experience into pre-sales - validate feasibility and estimates with delivery teams, run structured handover reviews, and stay engaged post-signoff to protect architectural integrity, resolve escalations and support customer acceptance on key engagements.

•         Customer Advisory: Act as a trusted AI advisor - run AI readiness and data maturity assessments, map business goals to AI roadmaps, identify quick wins, and propose phased implementation plans with clear ROI and success metrics.

•         Responsible AI & Governance: Embed security, data privacy, guardrails, evaluation and Responsible AI practices into every solution; address compliance, data residency and sovereign cloud requirements in regulated industries.

•         Team Mentoring: Coach and review the work of Solution Architects and Engineers; build reusable reference architectures, solution accelerators, estimation templates and playbooks that raise the quality and speed of the practice.

•         Thought Leadership: Contribute to case studies, whitepapers and knowledge bases, and represent Rapyder at customer events, webinars and partner forums (AWS, Microsoft, etc.,).

•         Market Intelligence: Track AI platform and ecosystem updates (AWS, Azure, GCP, NVIDIA, open-source LLMs, agent frameworks) and proactively bring relevant innovations into customer conversations.

What We’re Looking For

•         5+ years of experience in cloud solution architecture, AI/ML pre-sales or enterprise technology consulting, with a proven track record of designing and winning enterprise-scale AI/ML engagements.

•         Prior experience as a Delivery Engineer / Technical Lead on cloud, AI/ML projects - having owned technical delivery, managed scope, risks and customer expectations, and taken solutions from design to production.

•         Hands-on experience with AWS services (SageMaker, Bedrock, Lambda, Step Functions, S3, Glue, etc.) or Azure AI/ML services (Azure AI Foundry, Azure ML, Data Factory, etc.).

•         Strong working knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, evaluation, 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, monitoring and cost optimisation - with sound judgement on build-vs-buy and scalability trade-offs.

•         Demonstrated experience leading RFP responses, solution proposals and architecture documents, including effort estimation and commercial inputs.

•         Working knowledge of MLOps / LLMOps lifecycle: model versioning, CI/CD for ML, monitoring, drift detection and guardrails.

•         Strong communication and stakeholder management skills - able to present confidently to both technical architects and business executives, and to lead customer workshops independently.

•         Experience mentoring or guiding junior architects and collaborating across Sales, Delivery and Partner teams.

•         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.

•         Experience with Agile delivery, project governance and Agentic AI interoperability patterns (e.g., MCP, A2A).

•         Prior experience working in a cloud consulting or system integrator environment.