Senior AI Data Architect for Scalable, Trusted Data Systems

LS3002 GE Vernova Operations LLC

Town of Niskayuna (NY)

On-site

USD 138,000 - 230,000

Full time

3 days ago

Be an early applicant

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Benefits offered by this job

Relocation assistance

Competitive compensation

Excellent benefits

Job summary

GE Vernova Operations LLC seeks a Principal AI Data Architect to define and evolve the enterprise data architecture for trusted, scalable AI products. You will connect diverse data sources to AI agents, ensuring clean, accessible data for analytics and production‑grade capabilities.

You will lead architecture reviews, guide governance and security, and shape a multi‑year data roadmap across ingestion, storage, and AI consumption, partnering with product, engineering, and security teams.

Qualifications

Bachelor’s degree in computer science, Computer Engineering, Data Science or Information Systems or related field.
10+ years of experience as a Data Architect, Data Engineer, ML Ops or similar role with enterprise data systems.
Demonstrated ability to lead complex technical decisions and influence senior stakeholders, with strong communication skills and experience establishing data governance, security, privacy, quality, lineage, and operational standards in enterprise or regulated environments.
Demonstrated experience delivering data foundations for AI/ML or GenAI solutions, including one or more of RAG, vector databases, enterprise search, LLM evaluation, agentic workflows, feature/data pipelines, and model‑serving integration.
Proficiency in Python and SQL, with experience in data processing libraries.
Hands on experience with MCP Servers and MCP Registries.

Responsibilities

Enable AI and GenAI use cases: Ensure data is usable for LLM and ML applications, such as RAG, vector-based knowledge retrieval, agentic workflows, AI-assisted reporting, or secure data access for agents.
Establish data trust, security, and responsible‑AI controls including data quality and validation, lineage, metadata, privacy, RBAC/ABAC, auditability, retention, policy enforcement, and AI‑output evaluation and monitoring.
Drive technology, platform, and investment decisions by evaluating cloud data services, AI platforms, frameworks, and vendors; lead build‑versus‑buy assessments with explicit consideration of performance, resilience, security, operability, and total cost of ownership.
Define the target‑state AI data architecture and multi‑year roadmap for data ingestion, integration, storage, modeling, semantic access, and AI consumption; establish reusable reference architectures and engineering standards across domains.
Provide principal‑level technical leadership across the organization by leading architecture reviews, resolving complex cross‑domain design issues, partnering with product, engineering, security, and executive leaders.