AI Solution Architect

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 software engineering and solution architecture, including at least 3 years
designing AI/ML or data-driven solutions.
• Experience in pre-sales or client-facing technical roles within IT services, consulting, or an AI product
company.
• Cloud architecture certifications (AWS Solutions Architect – Professional, Azure Solutions Architect Expert,
or Google Professional Cloud Architect) are strongly preferred; TOGAF is a plus.
Nice to Have
• Experience with voice AI and contact centre solutions — speech-to-text, text-to-speech, IVR, and
telephony integration.
• Exposure to enterprise platforms such as SAP, IBM Maximo, Salesforce, or ServiceNow, and integrating AI
into them.
• Experience with MLOps / LLMOps tooling and production AI observability.
• Knowledge of agentic AI patterns, multi-agent systems, and the Model Context Protocol (MCP).
• Domain experience in utilities, healthcare, logistics, or financial services.

### Benefits
What We Offer

• The technical lead seat in a growing AI Solutioning practice, with direct influence on what we sell and how
we build it.
• Enterprise clients across diverse verticals — every engagement is a new architecture problem.
• A hands-on team of AI/ML and Full Stack engineers to bring your designs to life.
• Opportunity to shape Flatworld's reference architectures and AI technology strategy.
• Competitive compensation, learning budgets, certification support, and a path toward AI Practice Lead
roles.