Chief Architect-GRC

Key Responsibilities

Client Engagement & Solutioning

• Lead technical conversations with enterprise clients: discovery, requirements analysis, solution architecture, and value mapping.
• Conduct tailored demos and hands-on workshops for WorkNEXT AI modules (e.g., workflows, assistants, analytics, automation).
• Design proof-of-concepts (POCs) and pilot plans, including success criteria, data readiness, and deployment approach.
• Translate business needs into AI-driven use cases and solution designs (incl. integrations, data pipelines, governance).

Technical Ownership

• Build reference architectures and implementation playbooks for WorkNEXT AI modules across common enterprise stacks.
• Partner with engineering to scope features, validate feasibility, and drive technical issue resolution.
• Configure and optimize WorkNEXT AI models and workflows (prompt strategies, orchestration, guardrails, evaluation).
• Ensure compliance with security, privacy, and responsible AI standards; coordinate with InfoSec and legal teams.

Customer Success & Enablement

• Create client-facing technical collateral: solution briefs, runbooks, FAQs, architecture diagrams, ROI calculators.
• Train client admins and super-users; develop enablement plans and adoption campaigns.
• Monitor post-deployment health: performance metrics, drift, model evaluation, user feedback loops.

Cross-Functional Collaboration

• Capture client feedback and market signals to inform product roadmap and prioritization.
• Collaborate with Sales/Pre-Sales for deal strategy, estimates, and RFP responses.
• Work with Data Engineering and Integration teams to ensure robust pipelines and API/connector reliability.

Required Skills & Competencies

Technical

• Strong understanding of AI/ML foundations: NLP, LLMs, retrieval-augmented generation (RAG), prompt engineering, model evaluation.
• Experience with cloud platforms (Azure/AWS/GCP), containerization (Docker/Kubernetes), and API integration patterns (REST, GraphQL, webhooks).
• Knowledge of data pipelines (ETL/ELT), vector databases/embeddings, and observability of AI systems.
• Familiarity with enterprise security, compliance, and responsible AI (RBAC, PII handling, auditability, human-in-the-loop).
• Ability to create architecture diagrams, deployment runbooks, and performance monitoring strategies.

Client-Facing

• Exceptional communication—can simplify complex AI concepts for non-technical stakeholders.
• Consultative approach—discovery, problem framing, value story, and objection handling.
• Strong demo presence; comfortable tailoring demos to industry verticals and role personas.
• Negotiation and stakeholder management across business, IT, InfoSec, and procurement.

Mindset

• Outcome-oriented: focuses on measurable business impact, not just features.
• Curious and experimental: rapid prototyping, A/B testing, iterative improvement.
• Ownership and accountability: proactive risk management, transparent communication.

Qualifications

• 6–10+ years in Client-Facing Technical roles (e.g., Solutions Architect, Pre-Sales Engineer, AI Consultant).
• 3+ years hands-on with AI/ML, LLM applications, or automation platforms.
• Bachelor's/Master's in Computer Science, Data Science, Engineering, or equivalent experience.
• Experience in at least one enterprise vertical (e.g., BFSI, Manufacturing, Retail, Healthcare) is a plus.
• Certifications in cloud/AI (Azure AI Engineer, AWS ML Specialty, GCP ML Engineer) preferred.