Chief Architect-GRC

Role Summary

The WorkNEXT AI Tech Expert is the technical ambassador for WorkNEXT AI modules—owning end-to-end client engagement, discovery, solution design, demos, pilots, and technical enablement. This role translates business pain points into pragmatic AI solutions, drives adoption and outcomes, and partners with product, engineering, and customer success to ensure WorkNEXT deployments deliver measurable value at scale.

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).

• 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.

• 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.

• 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.

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.

• 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.

• 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.

• 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.

• Knowledge assistant with RAG for policy/SOP retrieval, multilingual Q&A.
• Process automation: ticket triage, claim summarization, email drafting, task routing.
• Insight generation: summarization of meetings/chats, trend analysis, risk alerts.
• Employee experience: HR policy assistant, IT helpdesk copilot, onboarding flows.
• Compliance & governance: redaction, PII detection, audit-ready conversation logs.

• Cloud & Infra: Azure (OpenAI, Cognitive Search), AWS (Bedrock, Sagemaker), GCP (Vertex AI).
• Data: Databricks/Snowflake/BigQuery; ETL tools (ADF, Airflow), vector stores (Pinecone, FAISS).
• Integration: REST/GraphQL APIs, iPaaS (MuleSoft, Boomi), event buses (Kafka).
• Observability & Evaluation: Prometheus/Grafana, MLflow, human eval tooling, prompt/version management.
• Security: OAuth2/OIDC, RBAC, KMS, DLP; Responsible AI guardrails & content filters.