AI Product Expert & Solutions Architect

Fresher

Job Description

Qualification

Minimum Requirements

Education: Bachelor's degree in Computer Science, Information Technology, Software Engineering, Information Systems, or a related quantitative technical field.
Certifications (Preferred but not mandatory): Professional certifications in Cloud Computing (AWS, Google Cloud, or Azure), Artificial Intelligence/Machine Learning, or Enterprise Architecture (e.g., TOGAF).

Related Experience

Proven Delivery Track Record: Minimum requirements prioritize a demonstrated capability to ship products over a fixed years-of-experience threshold. Must have a proven track record of independently designing and shipping at least one custom AI solution into a live, production-integrated enterprise system.
Enterprise Integration: Hands-on experience designing and deploying application integration architectures within complex enterprise environments (cloud-native, hybrid, or on-premises).
Consultative & Client-Facing Role: Demonstrated experience in customer-facing, presales, or technical consulting capacity—specifically translating ambiguous business requirements into concrete technical solutions.
Production Troubleshooting: Experience acting as an advanced technical escalation point (L3 support or equivalent lead engineer role) resolving post-deployment architectural bottlenecks or production anomalies.

Skills

Technical Skills

Generative & Agentic AI Architecture: Deep understanding of RAG (Retrieval-Augmented Generation) architectures (chunking strategies, vector databases, retrieval tuning) and hands-on experience with orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen, or similar).
Integration Patterns & Protocols: Mastery of REST API design/consumption, authentication patterns (OAuth, API keys), and async/event-driven integration (queues, webhooks). Familiarity with Model Context Protocol (MCP) or similar standards for tool-and-data integration is highly prioritized.
Core Engineering & Tools: Proficiency in Python and SQL. Practical experience with Git/version control, basic containerization (Docker), and cloud deployment practices.
Cost-Aware Systems Design: Ability to design AI solutions with practical engineering judgment, specifically modeling third-party API token costs, catching strategies, and optimizing infrastructure resources (e.g., computing/GPU requirements) to safeguard commercial margins.

Professional & Behavioral Competencies

Dealing with Ambiguity: Strong product instinct with the capability to drive project scope and move forward independently without fully specified briefs.
Consultative Communication: Exceptional ability to simplify highly complex AI technical concepts and present them persuasively to both technical teams (architects/delivery) and non-technical business executives.
Commercial & Business Acumen: Balanced mindset that aligns deep technical architecture decisions with execution speed, corporate business value, and operational viability.

Role Responsibilities

Role purposed

Product Strategy & MVP Development
Translate customer feedback and market needs into concrete technical insights.
Build rapid, early-stage Proof-of-Concepts (PoCs) for GenAI and Agentic workflows.
Evaluate emerging AI frameworks, protocols (e.g., MCP), and models for product readiness.
Technical Consulting & Customer Engagement
Lead deep-dive technical discovery and scope sessions with enterprise clients.
Present and defend complex AI architectures to enterprise architects and security teams.
Act as the trusted AI subject matter expert in high-value sales meetings.
Architecture Design & Technical Proposals
Design end-to-end integration and application architectures for production-grade AI solutions.
Author comprehensive technical sections for proposals, RFPs, and Scope of Work (SoW) documents.
Conduct cost-aware architecture analysis, estimating token usage and infrastructure needs.
Level 3 (L3) Delivery Support
Act as the final technical escalation point for post-deployment production anomalies.
Troubleshoot complex AI issues such as model drift, prompt injection, and agent routing failures.
Conduct post-mortem reviews on architectural failures to improve system resilience.
Capability Enablement & Knowledge Management
Create and maintain reusable technical assets, reference architecture, and demo kits.
Conduct training and enablement sessions for Sales, Presales, and Delivery teams.
Mentor technical delivery teams on advanced AI engineering tools