AI Solutions Architect

KDCI Outsourcing is looking for an AI Solutions Architect to help clients across industries harness the power of artificial intelligence. You will serve as a strategic bridge between cutting-edge AI technologies and real business goals — guiding clients from initial discovery through solution design, implementation, and adoption. You'll work closely with internal delivery teams, technical architects, and client stakeholders to design AI-driven strategies that are practical, scalable, and results-oriented.

Responsibilities:AI Strategy & Solution Design

• Partner with clients to understand their business challenges and design tailored AI solution roadmaps — from discovery to proof of concept to full deployment.

• Develop customized AI architectures covering data pipelines, ML models, NLP/LLM integrations, and system connectivity.

• Advise on AI platform selection (cloud-native, open-source, third-party SaaS) based on client maturity, budget, and technical environment.

• Lead Proof of Value (PoV) and pilot engagements, ensuring outcomes are measurable and tied to business KPIs.

Pre-Sales & Client Engagement

• Collaborate with business development and account teams to identify AI opportunities and craft compelling proposals and RFP responses.

• Deliver engaging, storytelling-driven demos and executive presentations that connect AI capabilities to specific client pain points.

• Lead technical discovery workshops, stakeholder interviews, and requirements-gathering sessions to shape scope and approach.

• Define success metrics and ROI frameworks that translate AI outputs into measurable business impact.

Technical Delivery & Oversight

• Guide delivery teams through solution architecture, integration design, data governance, and AI model evaluation and deployment.

• Advise on responsible AI principles: fairness, explainability, bias mitigation, and data privacy compliance (GDPR, NPC/DPA PH).

• Assess client data infrastructure readiness and provide recommendations on data engineering, labeling, and quality pipelines.

• Oversee QA of AI outputs during project delivery, ensuring alignment with client acceptance criteria.

Stakeholder Management

• Build trusted relationships with client stakeholders from IT and operations to C-suite decision-makers.

• Act as an ongoing advisor post-deployment — identifying new use cases, optimizing models, and ensuring continued ROI.

• Communicate complex AI concepts clearly to non-technical audiences through structured narratives and business-focused framing.