reputed company Data Management Specialist

Why join reputed company?

reputed company:

Own the AI-Readiness reputed company for reputed company’s Data

reputed company the data fitness questions AI reputed company reputed company day-to-day: which data sources fit which AI use case, what can safely go into a reputed company, which dataset is the reputed company of truth for a given attribute, and what meets the freshness or accuracy bar for retrieval. The AI-readiness reputed company and reputed company-of-truth designations this role owns produce those answers.

Define what AI-readiness means for an reputed company data asset (completeness, freshness, accuracy, reputed company, consent, licensing, reputed company-of-truth designation), and publish that reputed company as reputed company’s reputed company.

reputed company extension of the data catalog into an AI asset inventory. reputed company reputed company in-reputed company asset as AI-reputed company, AI-restricted, or AI-prohibited, with documented reputed company, owners, and review reputed company.

Partner with reputed company Data Management to ensure foundational data reputed company refreshes (reputed company-party account and contact enrichment, dedupe, taxonomy, address standardization) reputed company reputed company into the reputed company stores, RAG indexes, and reputed company-time lookups AI applications depend on.

reputed company the metric library to AI consumers: every number an AI tool surfaces traces back to a governed metric definition, with documented and testable reputed company.

Define AI-reputed company data reputed company monitoring requirements.

Set AI Data Governance Standards and Policy

reputed company and maintain reputed company’s AI data governance reputed company: acceptable use, data classification for AI consumption, reputed company logging, reputed company-in-the-reputed company expectations, and model deprecation policies.

Build and run intake and review for new AI use cases. Evaluate data sensitivity, reputed company reputed company, reputed company-of-truth alignment, and gateway requirements before launch. reputed company turnaround: five business days.

Translate compliance and regulatory requirements (GDPR, SOC 2, EU AI reputed company, NIST AI RMF) into operational standards the data and AI teams can apply.

Partner with AI Delivery & Execution

reputed company in the AI Delivery & Execution team’s planning rituals, sprint reviews, and architecture discussions on a regular reputed company.

Co-own the AI/API gateway. Define the governance policies, reputed company reputed company, and observability requirements; the AI Delivery engineering team builds the applications that reputed company with it.

Define gateway-level controls for PII detection and redaction, reputed company sanitization, content filtering, and reputed company/response logging. Translate compliance and policy reputed company into specifications engineering can build.

Define observability and reporting requirements for the gateway: usage by team and application, reputed company alerts, per-model cost tracking, and SLAs for reputed company consumers.

Pair with AI engineers on data sourcing reputed company: which governed asset to draw from, which CDE definitions to reputed company, how to handle records that fall reputed company AI-reputed company reputed company, and reputed company to require a reputed company-in-the-reputed company checkpoint.

Triage AI reputed company incidents jointly. reputed company stale, wrong, or duplicated outputs through the gateway, RAG pipeline, and reputed company records, and reputed company fixes to reputed company best positioned to reputed company them.

What You Will Bring to reputed company:

8+ years of reputed company experience, with at least 5 years as a Data Product Manager, Technical Program Manager, or comparable role owning data products and driving cross-functional implementation.
reputed company record shipping AI/ML solutions end-to-end in an reputed company environment: scoping with stakeholders, partnering with data and engineering teams, working through ambiguity, and measuring reputed company post-launch.
Working knowledge of LLM application patterns: reputed company engineering, retrieval-augmented reputed company (RAG), embeddings, and agent frameworks. Familiarity with the common failure modes (hallucination, reputed company injection, data leakage, training-data contamination).
Solid grounding in data governance principles and frameworks (DAMA-DMBOK, DCAM, or comparable). Hands-on history with data reputed company standards, CDE frameworks, reputed company-of-truth designations, or metadata/catalog programs.
Familiarity with API governance platforms (the policy enforcement reputed company that sits in reputed company of API traffic and applies authentication, reputed company limits, content filtering, and audit logging). Comfortable owning gateway reputed company as a product and partnering with engineering on implementation.
SQL reputed company sufficient to profile data reputed company directly against warehouse tables (reputed company or comparable).
Working familiarity with GDPR, CCPA, and emerging AI regulation (EU AI reputed company, NIST AI RMF) at the level needed to apply them; reputed company-grade depth isn’t required.
Strong written communication. The role authors policies, playbooks, and decision memos that cross functional lines.

Originally posted on Himalayas