Enterprise AI Architect - AI Product Design & Solutioning

Location: Hyderabad, India, Hybrid

Employment Type: Full-time

Experience: 8–12 years total, with at least 4 years in architecture roles and 2+ years building AI/ML solutions in production

Compensation: ₹32–48 LPA fixed

About Assentcode Technologies

Assentcode Technologies is a Hyderabad-based software services company, founded in 2019 by a group of entrepreneurial engineers. We help businesses across the globe build, modernize, and scale their technology, with offices in Hyderabad and Frisco, Texas (USA).

Our core services include:

• Product Engineering (product design and development, web and mobile apps, UI/UX)
• Data Analytics, AI/ML, chatbots, and virtual assistants
• Cloud solutions, DevOps, and CI/CD
• Legacy modernization and digital transformation
• Custom enterprise software and quality engineering

We are a lean, fast-moving team, and AI is one of our biggest growth areas. We are looking for an enterprise architect who can turn a client’s business problem into a well-designed, production-grade AI solution.

About the Role

As our Enterprise AI Architect, you will own the technical design of AI products and solutions for our enterprise clients, from the first discovery conversation to production architecture. You will decide what to build, which models and patterns to use, how it integrates with the client’s systems, and how it stays secure, reliable, and cost-effective at scale. This is a hands-on architecture role: you will define the blueprint, guide the delivery team, and stay close enough to the code to validate that what’s built matches what was designed.

You will also work closely with our business development team as the technical voice in client conversations, so you should be comfortable explaining complex AI concepts to both engineers and executives.

What You Will Own

Client Problem → Discovery → Solution Architecture → Proof of Concept → Estimation and Proposal → Delivery Guidance → Production Readiness → Continuous Improvement

Key Responsibilities

Solution Design and Architecture

• Translate business problems into AI solution architectures, including GenAI/LLM applications, RAG systems, AI agents, predictive models, and intelligent automation
• Select the right approach for each problem: build vs. buy, open-source vs. proprietary models, fine-tuning vs. RAG vs. prompt engineering, and when AI is not the right answer
• Design end-to-end architectures covering data ingestion, vector stores, model serving, orchestration, APIs, integration with enterprise systems, and user-facing applications
• Create architecture documents, solution blueprints, and technical diagrams that delivery teams can build from

Client and Pre-Sales Engagement

• Join discovery workshops with CTOs, CIOs, and business leaders to understand objectives, constraints, and data readiness
• Support the business development team with technical proposals, SOW inputs, effort estimates, and architecture presentations
• Build and demo proofs of concept to validate feasibility and win client confidence
• Present trade-offs, risks, and timelines honestly, and manage client expectations on what AI can and cannot deliver

Delivery Leadership

• Provide technical direction to engineering, data, and DevOps teams during implementation
• Review designs and code for architectural integrity, scalability, and maintainability
• Define standards for LLMOps/MLOps: CI/CD for models, monitoring, versioning, evaluation, and rollback
• Mentor engineers and help build our internal AI capability

Quality, Security, and Governance

• Establish evaluation frameworks for AI quality: accuracy, hallucination rates, latency, cost per query, and bias
• Design for data privacy, security, access control, and compliance (e.g., GDPR, SOC 2, HIPAA where relevant)
• Apply responsible AI practices, including guardrails, human-in-the-loop design, and auditability
• Optimize for performance and cloud cost without compromising reliability

Innovation and Thought Leadership

• Track the fast-moving AI landscape (models, frameworks, tools, and best practices) and decide what is ready for enterprise use
• Build reusable accelerators, reference architectures, and templates that speed up future projects
• Contribute to case studies, technical content, and our AI service positioning

Must-Have Requirements

• 8–12 years of experience in software engineering, data engineering, or ML, with at least 3 years in an archite…