Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field; equivalent practical experience considered.
Proficiency in Python (or comparable) and modern AI development tooling (e.g., Claude Code, Cursor, GitHub Copilot).
Hands-on experience with LLM application patterns - prompt engineering, RAG, function/tool calling, agentic orchestration, and MCP.
Familiarity with leading LLMs (Anthropic Claude, OpenAI GPT/o-series, Google Gemini, Meta Llama, Mistral) and model selection trade-offs (reasoning depth, context window, cost, latency, data residency).
Ability to translate GRC domain requirements into well-governed, production-grade automations with human-in-the-loop controls.
Experience building agent evaluation frameworks, guardrails, prompt/version management, and observability/logging for production LLM systems.
Preferred experience
Experience integrating with enterprise platforms and APIs; familiarity with GRC tooling (e.g., ServiceNow IRM, Archer, AuditBoard) a plus.
Qualification wording
Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field; equivalent practical experience considered.
Proficiency in Python (or comparable) and modern AI development tooling (e.g., Claude Code, Cursor, GitHub Copilot).
Hands-on experience with LLM application patterns - prompt engineering, RAG, function/tool calling, agentic orchestration, and MCP.
Familiarity with leading LLMs (Anthropic Claude, OpenAI GPT/o-series, Google Gemini, Meta Llama, Mistral) and model selection trade-offs (reasoning depth, context window, cost, latency, data residency).
Ability to translate GRC domain requirements into well-governed, production-grade automations with human-in-the-loop controls.
Experience building agent evaluation frameworks, guardrails, prompt/version management, and observability/logging for production LLM systems.
Experience integrating with enterprise platforms and APIs; familiarity with GRC tooling (e.g., ServiceNow IRM, Archer, AuditBoard) a plus.
Education & alternatives
Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field; equivalent practical experience considered.
- Minimum 4+ years in software/AI engineering, with hands-on experience building LLM-powered applications and agentic workflows.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Python
AWS
Azure
GCP
SageMaker
Where the posting names them
- Minimum 4+ years in software/AI engineering, with hands-on experience building LLM-powered applications and agentic workflows.
- Proficiency in Python (or comparable) and modern AI development tooling (e.g., Claude Code, Cursor, GitHub Copilot).
- Hands-on experience with LLM application patterns - prompt engineering, RAG, function/tool calling, agentic orchestration, and MCP.
- Experience building agent evaluation frameworks, guardrails, prompt/version management, and observability/logging for production LLM systems.
- Familiarity with cloud ML platforms (AWS Bedrock/SageMaker, Azure AI, GCP Vertex) and CI/CD-integrated deployment gates.
- AI governance certification a plus (e.g., IAPP AIGP), including agentic architecture concepts.
Yes. We’ve followed one other AI Data Engineer III job there since September 2026. It is still up.
ManilaStill up after 65 days, first seen Sep 2
This jobUp 64 days so far, first seen Sep 3
Sep 2Today
Demand for Data Engineer roles
New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
August 17Last week
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
This role reports into Director/Sr. Manager within Technology GRC and is the technical builder behind the function's agentic AI transformation. The AI Data Engineer III designs, builds, and operates the data pipelines, AI agents, and automations that deliver GRC services — with primary focus on the Data Governance capabilities (e.g., data quality, data lineage and data catalog), all operating under a human-in-the-loop model supervised by the responsible GRC leader. The role bridges GRC domain requirements and engineering execution: building the data foundations (ingestion, modeling, quality) that agentic workflows depend on, and prototyping control automations, evidence collectors, and governance tooling that reduce manual effort and enable the team to scale without proportional headcount growth.
Responsibilities
GRC Tool & Automation Engineering
Build and integrate agentic AI solutions and automations within the enterprise GRC platform to standardize workflows, automate evidence collection, and improve reporting.
Develop control automations, evidence collectors, and governance tooling, reducing dependence on the broader engineering backlog.
Risk Register Automation
Engineer AI-assisted workflows to populate, maintain, and reconcile the enterprise Risk Register across technology domains.
Automate linkage between risk entries, controls, and remediation tracking.
Policy Governance Automation
Build AI-assisted tooling for policy drafting, framework crosswalks, annual review cadence, and exception workflows.
Design agentic workflows using RAG, function/tool calling, and Model Context Protocol (MCP), with appropriate guardrails for accuracy, confidentiality, and IP boundaries.
Implement human-in-the-loop checkpoints and monitoring so GRC leaders can supervise and validate agent outputs.
Apply AI risk controls aligned to OWASP Top 10 for LLM Applications, NIST AI RMF, ISO/IEC 42001, and MITRE ATLAS.
Agent Evaluation, Observability & Guardrails
Build evaluation harnesses and observability for deployed agents - measuring grounding, accuracy, and consistency while minimizing hallucinations across GRC use cases.
Implement guardrails, deployment gates, and immutable audit trails/logging so non-compliant or low-confidence outputs are caught before use.
Maintain model documentation (model cards, data provenance) to support AI governance and regulatory defensibility.
Qualifications
Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field; equivalent practical experience considered.
Minimum 4+ years in software/AI engineering, with hands-on experience building LLM-powered applications and agentic workflows.
Proficiency in Python (or comparable) and modern AI development tooling (e.g., Claude Code, Cursor, GitHub Copilot).
Hands-on experience with LLM application patterns - prompt engineering, RAG, function/tool calling, agentic orchestration, and MCP.
Familiarity with leading LLMs (Anthropic Claude, OpenAI GPT/o-series, Google Gemini, Meta Llama, Mistral) and model selection trade-offs (reasoning depth, context window, cost, latency, data residency).
Working knowledge of the AI/LLM risk landscape: OWASP Top 10 for LLM Applications, NIST AI RMF, ISO/IEC 42001, MITRE ATLAS, and emerging regulation (EU AI Act, NYDFS AI guidance).
Experience integrating with enterprise platforms and APIs; familiarity with GRC tooling (e.g., ServiceNow IRM, Archer, AuditBoard) a plus.
Ability to translate GRC domain requirements into well-governed, production-grade automations with human-in-the-loop controls.
Strong collaboration skills and the ability to partner with non-technical GRC stakeholders.
Experience building agent evaluation frameworks, guardrails, prompt/version management, and observability/logging for production LLM systems.
Familiarity with cloud ML platforms (AWS Bedrock/SageMaker, Azure AI, GCP Vertex) and CI/CD-integrated deployment gates.
AI governance certification a plus (e.g., IAPP AIGP), including agentic architecture concepts.
Preferred experience in the Property Management, Multifamily Housing, SaaS, FinTech, or PropTech industries.