Chief Architect-GRC

Key Responsibilities

Client Engagement & Solutioning

• Own and lead technical discussions with enterprise clients, covering discovery, requirements analysis, solution architecture, and value mapping.
• Deliver tailored demos and interactive workshops for WorkNEXT AI modules, including workflows, assistants, analytics, and automation.
• Develop proof-of-concepts (POCs) and pilot plans, including clearly defined success criteria, data readiness assessment, and deployment approach.
• Convert business objectives into AI-driven use cases and solution designs, including integrations, data pipelines, and governance.

Technical Ownership

• Create reference architectures and implementation playbooks for WorkNEXT AI modules across widely used enterprise technology stacks.
• Collaborate with engineering teams to define feature scope, confirm feasibility, and drive resolution of technical issues.
• Configure and continually improve WorkNEXT AI models and workflows, including prompt strategies, orchestration, guardrails, and evaluation.
• Support adherence to security, privacy, and responsible AI standards; coordinate with InfoSec and legal stakeholders.

Customer Success & Enablement

• Produce client-ready technical assets such as solution briefs, runbooks, FAQs, architecture diagrams, and ROI calculators.
• Train client administrators and super-users by creating enablement plans and running adoption campaigns.
• Track post-deployment performance and health, including performance metrics, drift, model evaluation, and user feedback loops.

Cross-Functional Collaboration

• Gather client feedback and capture market signals to guide product roadmap input and prioritization.
• Partner with Sales/Pre-Sales to support deal strategy, estimates, and contributions to RFP responses.
• Work alongside Data Engineering and Integration teams to ensure reliable data pipelines and consistent API/connector performance.

Required Skills & Competencies

Technical

• Solid understanding of AI/ML fundamentals: NLP, LLMs, retrieval-augmented generation (RAG), prompt engineering, and model evaluation.
• Hands-on experience with cloud platforms (Azure/AWS/GCP), containerization (Docker/Kubernetes), and API integration patterns (REST, GraphQL, webhooks).
• Knowledge of data pipeline concepts (ETL/ELT), vector databases/embeddings, and observability practices for AI systems.
• Familiarity with enterprise security, compliance, and responsible AI, including RBAC, PII handling, auditability, and human-in-the-loop.
• Capability to produce architecture diagrams, deployment runbooks, and performance monitoring strategies.

Client-Facing

• Outstanding communication skills with the ability to explain complex AI concepts to non-technical stakeholders.
• Consultative mindset—leads discovery, problem framing, value narrative development, and objection handling.
• Strong demo capability; able to tailor demos to industry verticals and specific role personas.
• Skilled in negotiation and stakeholder management across business, IT, InfoSec, and procurement.

Mindset

• Outcome-oriented focus on achieving measurable business impact, not only delivering features.
• Curious and experimental approach, including rapid prototyping, A/B testing, and iterative improvement.
• Demonstrated ownership and accountability with proactive risk management and transparent communication.

Qualifications

• 6–10+ years in Client-Facing Technical roles (e.g., Solutions Architect, Pre-Sales Engineer, AI Consultant).
• 3+ years of hands-on experience with AI/ML, LLM applications, or automation platforms.
• Bachelor's/Master's degree 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.