Technical Presales | Discovery | Solution Architecture | Deployment | Managed Services

Experience-5–10 years of relevant experience

Function-AI & Data Practice

Role type

Customer-facing, hands-on individual contributor

Location- Noida

Reporting to-VP-AI & Data Practice

Role overview

AppSquadz is looking for a hands-on AI Solutions Architect who can own the technical customer journey—from first discovery and solution shaping through architecture, proof of concept, production deployment and managed-services recommendations. The role combines consultative technical presales with practical engineering judgment and accountability for secure, scalable, commercially viable AI solutions. The architect will collaborate with sales, alliances, delivery and customer stakeholders while remaining actively involved in implementation and handover.

Key responsibilities

- Technical presales and solution shaping: Partner with AI Services Business Development Manager in customer meetings; qualify AI opportunities, explain solution options, run technical demonstrations and contribute to proposals, effort estimates, bills of materials, statements of work and technical responses to RFPs.
- Discovery workshops and use-case assessment: Plan and facilitate business/IT workshops; map workflows, data sources, stakeholders, pain points, security constraints and success metrics. Assess AI readiness, prioritize use cases by value, feasibility and risk, and document requirements, assumptions and dependencies.
- Architecture and solution design: Design end-to-end generative AI, agentic AI, ML and data-enabled architectures covering models, prompts, RAG, vector search, orchestration, APIs, integrations, identity, guardrails, observability and cloud infrastructure. Produce diagrams, design decisions, cost estimates and deployment patterns.
- PoCs, deployment and productionization: Build or guide working PoCs and pilots; establish acceptance criteria; support integration, CI/CD, evaluation, performance testing, security reviews and production rollout. Troubleshoot issues and support operational readiness,



documentation and knowledge transfer.
- Managed-services advisory: Assess post-go-live support needs and recommend appropriate managed-services packages: monitoring, incident response, model/prompt evaluation, cost optimization, data and knowledge-base refresh, security controls, SLA/SLO reporting and continuous improvement. Define scope, responsibilities, service levels and handover needs with delivery teams.
- Collaboration and governance: Work across OEM partners, engineering and customer teams; apply responsible-AI, privacy, data residency, governance and compliance considerations. Communicate trade-offs clearly to both business executives and technical teams.

Required experience and skills

- Experience: 5–10 years in solution architecture, cloud/data engineering, AI/ML engineering or technical consulting, including meaningful customer-facing presales and hands-on implementation experience.
- AI engineering: Practical experience delivering at least one production AI/GenAI solution; knowledge of LLM APIs, embeddings, RAG, agent workflows, evaluation, hallucination mitigation and prompt engineering.
- Cloud and platforms: Working experience with AWS and its AI/data services; familiarity with Amazon Bedrock and at least one major model ecosystem such as Anthropic Claude or OpenAI. Equivalent Azure/GCP experience is useful.
- Engineering and integration: Robust understanding of Python, REST APIs, application integration, databases, vector stores, containers, CI/CD, infrastructure as code and production monitoring.
- Security and economics: Ability to design IAM, encryption, secrets management, network controls,



data protection and human oversight; estimate infrastructure/model usage costs and explain architectural trade-offs.
- Communication: Confidence facilitating workshops, whiteboarding solutions, writing technical proposals and presenting recommendations to CIO/CTO stakeholders and engineering teams.

Preferred qualifications

- Additional experience: AWS Solutions Architect or relevant cloud/AI certification; experience with agent orchestration frameworks, MLOps/LLMOps, regulated-industry architectures, enterprise knowledge assistants, coding assistants or intelligent process automation.
- Commercial acumen: Experience translating technical solutions into delivery plans, effort estimates, recurring support offerings and measurable customer outcomes.

Expected deliverables

- Discovery: Workshop agenda, current-state assessment, prioritized use-case backlog and measurable success criteria.
- Solution design: Reference/target architecture, integration and security design, sizing, cost estimate, risks and implementation roadmap.
- Execution: PoC/demo, deployment plan, test and evaluation results, production handover documents.
- Operations: Managed-services recommendation, operating model, monitoring plan, SLA/SLO inputs and improvement backlog.

How success will be measured

- Quality and conversion of technically qualified opportunities and discovery workshops.
- Clarity, feasibility and accuracy of proposed architectures, effort estimates and solution costs.
- PoC-to-production progression, deployment quality and customer acceptance.
- Security, reliability, operational readiness and customer satisfaction after go-live.
- Practical managed-services attach opportunities and smooth transition to support teams.

Candidate profile

We value an architect who can engage a business leader in the morning, whiteboard an enterprise AI design with the technology team, and work with engineers to make the solution run reliably in production. This is not a presentation-only presales role or a purely back-end development role.

Please share your CV at [email protected]

📌 AI Solutions Architect (Noida)
🏢 AppSquadz
📍 Noida