AI Data & Security Governance Engineer (MAD-BS-OR)

Hitachi•Hillsboro, OR

6d•$134,476 - $184,905•Hybrid

About The Position

As an AI Data & Security Governance Engineer (AIE) at HTA, you will build the technical guardrails that allow our distributed engineering and data science teams to innovate safely. Instead of just writing policies, the AIE will implement ‘governance as code.’ The AIE will design and deploy automated security controls, manage data access infrastructure, and integrate model evaluation and security checks directly into our CI/CD pipelines. The AIE will work closely with HTA divisions to ensure that our data storage, LLM deployments, and application architecture remain secure, compliant, and highly performant.

Requirements

Master’s degree in Computer Security, AI Engineering/Governance, Software Engineering, or related field or equivalent combination of education and experience
Minimum of five (5) years of software, data, or security engineering experience
Minimum of five (5) years of experience of programming proficiency in Python, SQL, Bash, Go, or similar languages used in data and infrastructure engineering
Minimum of five (5) years of experience with AI/ML Ops deploying and securing machine learning models (including generative AI/LLMs) in production environments
Minimum of five (5) years of experience with DevOps & Automation CI/CD pipelines, Git workflows (e.g., monorepo architecture), and infrastructure-as-code tools
Minimum of five (5) years of experience in Governance platforms such as Collibra, Immuta, Privacera etc.
Minimum of five (5) years of experience with frameworks and audits such as NIST AI RMF, ISO 42001, OWASP LLM Top 10
Minimum of one (1) year of hands-on experience implementing AI guardrails and moderation pipelines (e.g., NeMo Guardrails, Llama Guard, Guardrails AI)
Minimum of one (1) year of experience in AI red teaming, adversarial robustness testing, and hallucination/bias monitoring
Deep understanding of data security principles (encryption, IAM, secure network architecture)
Familiarity with cloud platforms and distributed storage architecture
Knowledge of AI vulnerabilities (e.g., OWASP Top 10 for LLMs) and mitigation strategies
Ability to work under pressure and with aggressive release timelines
Ability to work in a challenging, small team environment (less than 10 members)
Ability to prioritize and multitask between multiple responsibilities
Must have strong problem-solving skills
Demonstrate strong work ethics
Demonstrate strong verbal and written communication skills with people at all levels within the organization and outside of the company
Ability to quickly transport from location to location (i.e., air, car, etc.)
Must comply with all corporate safety requirements and directives
Expected to use Personal Protective Equipment (PPE) when required
Follow all equipment-specific safety protocols

Nice To Haves

Minimum of five (5) years of experience working with distributed teams across multiple geographic regions
Familiarity with compliance frameworks (GDPR, CCPA) and translating them into technical requirement

Responsibilities

Design, implement, and maintain granular Role-Based Access Control (RBAC) and identity management across our cloud infrastructure and internal tools
Build automated data masking, anonymization, and encryption mechanisms into data ingestion and processing pipelines
Secure distributed storage solutions and ensure secure configurations across compute instances and databases
Cloud IAM hardening, secret management (e.g., HashiCorp Vault, AWS Secrets Manager), and zero-trust data access patterns
Secure vector database architecture (e.g., Milvus, Pinecone, pgvector) with tenant isolation and metadata-level filtering
Engineer and deploy technical safeguards for large language models (e.g., Llama or similar architecture), including input/output filtering to prevent prompt injection and data leakage
Integrate automated security scanning, fairness/bias testing, and model performance evaluations directly into standard CI/CD deployment manuals
Build telemetry and alerting systems to track model drift, anomalous API usage, and compliance adherence in real-time
Develop automated processes to extract and catalog metadata, ensuring end-to-end data lineage is tracked programmatically
Build automated reporting scripts and dashboards to provide continuous visibility into data access logs and model compliance for security audits
Other duties as assigned