Back
Metro location and strong AI demand increase competition, but senior specialization reduces applicant pool.
Requires specialized AI architecture, LLM, and productionization experience, limiting cross-industry transferability.
Explicit 8–12 years and mandatory GenAI, LLM, cloud, and leadership requirements make filters highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end delivery of AI use-cases from problem framing to production launch, managing MVPs through UAT and operational handover.
Architect and lead delivery of multiple concurrent enterprise-grade GenAI solutions including RAG, agentic workflows, ensuring MVP readiness within 4–6 weeks.
Establish architectural guardrails for data, models, orchestration, and deployment while ensuring compliance with security, privacy, and governance standards.
8–12 years of experience in AI solution architecture or applied AI delivery.
Proven experience delivering AI/GenAI solutions from concept to production in a fast-paced environment.
Strong knowledge of LLM ecosystems, embeddings, vector databases, agent frameworks, and cloud AI services.
Solid software engineering fundamentals including CI/CD, infrastructure automation, and experience implementing data privacy and governance controls.
Experienced in managing multiple fast-paced AI/GenAI projects simultaneously with a focus on rapid MVP delivery and production rollout.
Skilled at bridging business and technology through direct stakeholder engagement and translating business problems into structured AI solutions.
Technical leadership style focused on mentoring mid-size engineering teams (10-15) to maintain velocity and design consistency while navigating agile delivery frameworks.