AI & Data Solution Architect

Position: Role is to is to build a robust AI Use Case Implementation solution with staffing plan, timelines, effort estimates, technical stack identification, foundational fixes, suggestions, etc.

Location: Anywhere in India

Work Mode: Remote

AI Solution Architect

1. AI Solution Design: Strong experience translating business use cases into practical, implementable AI/ML solution architectures.
2. Use Case Feasibility: Able to assess whether a use case is technically feasible and identify major implementation blockers early.
3. AI/ML Architecture: Strong understanding of ML, GenAI, predictive analytics, optimization, agentic AI, and appropriate model selection.
4. Business-to-Technology Translation: Can work directly with Procurement, Pricing, and Demand Planning SMEs and convert business requirements into solution components.
5. Enterprise Integration: Experience designing AI solutions that integrate with ERP, CRM, planning, procurement, data platforms, and other enterprise systems.
6. Data Requirement Definition: Able to define the data, history, granularity, quality, features, and refresh frequency required for AI models to perform effectively.
7. Model Performance & Validation: Understands how to define model accuracy, confidence, testing, explainability, monitoring, and acceptance criteria.
8. Human-in-the-Loop Design: Can determine where AI provides recommendations versus where business users review, approve, override, or make decisions.
9. Implementation Estimation: Able to translate the proposed architecture into realistic development scope, effort, resource requirements, timeline, and cost estimates.
10. Executive Communication: Can clearly explain solution choices, trade-offs, risks, dependencies, and feasibility recommendations to business and technology leadership.

Data Architect

1. Data Readiness Assessment: Strong ability to evaluate whether available data is sufficient, reliable, and usable for the prioritized AI use cases.
2. Source-System Analysis: Experience working with ERP, procurement, pricing, sales, forecasting, inventory, customer, supplier, and external data sources.
3. Data Mapping & Lineage: Able to trace required data from source systems through transformations to the AI model and business output.
4. Data Quality Assessment: Can identify completeness, accuracy, consistency, duplication, granularity, historical coverage, and master-data issues.
5. AI Data Requirements: Understands the data structures, features, history, training datasets, and refresh requirements needed for predictive AI/ML solutions.
6. Integration Architecture: Can define practical approaches for extracting and integrating data through APIs, databases, ETL/ELT pipelines, files, or data platforms.
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8. Gap & Remediation Planning: Can clearly identify missing data and foundational gaps and recommend practical fixes required before or during implementation.
9. Effort & Cost Estimation: Able to estimate data engineering effort, integration complexity, infrastructure needs, dependencies, and implementation costs.
10. Cross-Functional Collaboration: Can work closely with the AI Architect, business SMEs, client IT teams, and system owners to develop an implementation-ready data solution.

Short Term Assignment. Architects with any one of the below Skills can Apply.

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