AI Architect

Solution Architecture & Delivery Design: secure end‑to‑end AI solution architectures including data ingestion, model training, inference pipelines, orchestration flows, and integration with downstream systems. Implement architectures defined by the Director Enterprise Systems, ensuring alignment with standards, patterns, and platform strategy. Build GenAI solutions using RAG, vector search, grounding strategies, prompt orchestration, and model evaluation frameworks. Create and maintain high‑quality HLD/LLD documentation, sequence diagrams, and data flows for AI workloads.

Perform hands‑on technical proofs of concept, evaluate models/tools, and convert prototypes into production‑grade systems.

AI Engineering Enablement: build secure reusable code templates, libraries, and patterns for deployment, evaluation, and monitoring of workloads. Partner with engineers to integrate AI components into pipelines, data products, and operational workflows. Implement model lifecycle management: versioning, experimentation tracking, registry integration, automated deployment.

Data & Retrieval Architecture: architect federated data access patterns for AI, integrating multiple source systems (data lakes, warehouses, content repositories, SaaS platforms) into cohesive retrieval pipelines. Design data pipelines that support AI use cases: feature engineering, embedding generation, chunking strategies, and retrieval flows. Implement and optimize vector DB schemas, embeddings, and hybrid search (keyword + semantic) patterns. Ensure data quality, lineage, access controls, and privacy protections align with enterprise requirements.

Governance, Security & Compliance: partner with Security and Privacy teams to ensure AI solutions align with applicable regulations and standards, translating policy requirements into enforceable technical controls. Apply responsible AI principles, ensuring solutions include safety, bias mitigation, grounding, and hallucination safeguards. Establish guardrails for model training and prompt usage, including restrictions on sensitive data ingestion and prevention of model leakage. Implement enterprise‑approved security patterns (private endpoints, tokenization/MPC, encryption, IAM roles, network controls).

Conduct architecture reviews, risk assessments, and model evaluations as part of deployment readiness.

Collaboration & Communication: collaborate with product managers, engineers, and business stakeholders to refine requirements and translate them into technical specifications. Provide clear technical guidance, mentoring, and code reviews for teams using AI services. Communicate trade‑offs, limitations, and risks of different AI approaches to both technical and non‑technical audiences. Maintain a strong business perspective to ensure systems are implemented in ways that support operational goals and user needs.

Ensure solutions meet high standards of quality, with successful delivery driven by thorough testing and validation practices. Provide technical guidance to other engineering team members, fostering growth and knowledge sharing.