AI Solution Architect

Job Information

Date Opened 10/05/2026

Job Type Full time

Industry Technology

City Bangalore North

State/Province Karnataka

Country India

Zip/Postal Code 560077

Job Description

About Flatworld Solutions

Flatworld Solutions is a leading IT and business services company delivering end-to-end technology solutions

across software development, data management, AI/ML, and digital transformation. Our AI Solutioning team is the

engine behind intelligent products and platform-driven engagements — translating enterprise challenges into

scalable, AI-powered outcomes for clients worldwide.

Role Summary

We are looking for an AI Solution Architect to be the technical authority of our AI Solutioning team. You will turn

client problems into buildable AI architectures — choosing models, designing data flows and integrations, sizing

infrastructure, and estimating what a solution will cost to build and to run.

The role spans pre-sales and delivery. In pre-sales, you will shape the technical side of proposals, answer

architecture questions in front of clients, and make sure what we sell can be built at the price we quote. In

delivery, you will guide our AI/ML and Full Stack engineers, own design decisions, and keep solutions secure,

scalable, and maintainable. The ideal candidate has designed and shipped enterprise AI systems and is equally

credible with a CTO and with an engineering team.

Key Responsibilities

A. Solution Architecture & Technical Design

Design end-to-end AI solution architectures covering models, data pipelines, retrieval, agents,

integrations, user interfaces, hosting, and security.

Choose the right approach for each problem — LLM, classical ML, rules, or process automation — and

document the trade-offs clearly.

Define integration patterns with client systems: CRMs, ERPs, asset management platforms, contact centre

and telephony platforms, document repositories, and data warehouses.

Produce and maintain architecture artefacts

Solution Architecture Documents and high-level / low-level design documents

Architecture diagrams — context, component, data-flow, and deployment views

Non-functional requirements

performance, availability, scalability, and security

Architecture Decision Records (ADRs) for key technology choices

B. Pre-Sales & Technical Proposal Support

Join discovery workshops with the AI Solutions Lead and business analysts; assess the client's technology

landscape, data maturity, and integration readiness.

Own the technical sections of proposals, RFP/RFI responses, and SOWs — architecture, technology stack,

assumptions, dependencies, and risks.

Produce effort estimates and running-cost projections (model/token spend, compute, storage, licences)

that feed pricing and client ROI models.

Present architectures and technical demos to client IT teams, CIOs, and CTOs; answer feasibility, security,

and scalability questions with confidence.

Validate that every proposed solution can be delivered within the quoted scope, timeline, and budget.

C. Delivery Oversight & Technical Leadership

Act as design authority during delivery — review designs and code, approve technical decisions, and

resolve architecture-level issues.

Guide AI/ML and Full Stack Engineers on patterns, frameworks, and best practices; unblock them quickly.

Define acceptance criteria for AI components: accuracy targets, latency budgets, and cost per transaction.

Identify technical risks, data gaps, and scaling bottlenecks early; escalate with options, not just problems.

Plan the path from prototype to production — hardening, observability, and support handover.

D. Security, Governance & Responsible AI

Design for data privacy and security: PII handling, data residency, access controls, encryption, and audit

logging.

Ensure solutions meet applicable regulations and client policies (GDPR, HIPAA, India's DPDP Act, SOC 2, ISO

27001).

Build in AI guardrails — prompt injection defences, content filtering, human-in-the-loop checkpoints, and

output monitoring.

Respond to client security questionnaires and support information security reviews.

E. Reference Architectures & Practice Building

Build Flatworld's library of reusable reference architectures, solution accelerators, and estimation

templates for common AI use cases.

Evaluate new models, platforms, and frameworks; maintain a recommended technology stack with clear

guidance on when to use what.

Mentor engineers and business analysts on architecture thinking and technical communication.

Contribute to thought leadership — whitepapers, internal tech talks, and client-facing points of view on AI

adoption.

Requirements

Required Skills & Competencies

Proven experience designing and delivering enterprise AI/ML solutions — LLM applications, RAG,

conversational AI, document intelligence, or predictive analytics.

Strong grounding in software and cloud architecture: microservices, event-driven design, APIs, and

enterprise integration patterns.

Working knowledge of the LLM ecosystem — commercial and open-weight models, embeddings, vector

databases, agent frameworks, and evaluation methods.

Hands-on depth in at least one major cloud (AWS, Azure, or GCP) and its AI services (Bedrock, Azure

OpenAI / AI Foundry, Vertex AI).

Ability to estimate build effort and running costs accurately, and defend those numbers in front of clients.

Solid understanding of data architecture: data lakes, warehouses, ETL/ELT pipelines, and data governance.

Security-first design mindset, with experience delivering in regulated industries.

Excellent client-facing communication — comfortable explaining complex architectures to C-suite and

engineering audiences alike.

Enough hands-on coding ability (Python and/or TypeScript) to build proofs of concept and review

engineering work credibly.

Qualifications

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related

field.

8 – 12 years of experience in softwar