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Vector Databases Jobs in Lincoln, NE (NOW HIRING)

CyberSecurity AI Engineer

Lincoln, NE · On-site

$115K - $155K/yr

... vector databases, and training pipelines. • Build automated tools to detect and mitigate AI-related risks, such as anomalous model outputs and prompt injection attempts. • Conduct security ...

Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

What are vector databases?

Vector databases are specialized databases designed to store, manage, and search high-dimensional vector data, which is commonly generated from machine learning models, such as embeddings from natural language processing or image recognition. They enable efficient similarity search operations, such as finding the most similar items to a given query vector, which is essential for applications like recommendation systems, semantic search, and AI-powered search engines. Unlike traditional databases that handle structured or unstructured data, vector databases are optimized for fast and scalable similarity searches on large datasets of vectors.

What are some common challenges faced when working with vector databases, and how can they be addressed?

Professionals working with vector databases often encounter challenges such as efficiently scaling to handle large datasets, ensuring low-latency similarity searches, and integrating the database with machine learning pipelines. To address these, teams typically implement distributed architectures, fine-tune indexing strategies, and collaborate closely with data engineers and machine learning specialists. Staying updated with the latest developments in vector database technologies and maintaining clear communication with cross-functional teams are also key to overcoming these challenges.

What is the difference between Vector Databases vs Data Engineers?

AspectVector DatabasesData Engineers
Required SkillsDatabase management, data modeling, query optimizationData pipeline development, ETL processes, programming
Work EnvironmentData storage systems, AI/ML projects, cloud platformsData infrastructure, cloud environments, big data tools
Industry UsageAI, machine learning, recommendation systemsData integration, analytics, data architecture

While Vector Databases focus on storing and querying high-dimensional vector data for AI applications, Data Engineers build and maintain data pipelines and infrastructure to support data analysis and machine learning workflows. Both roles are essential in data-driven industries but serve different functions within the data ecosystem.

What can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

What are the key skills and qualifications needed to thrive as a Vector Database Engineer, and why are they important?

Success as a Vector Database Engineer requires a strong background in computer science, database management, and experience with machine learning or AI-driven data systems. Familiarity with vector database platforms (such as Pinecone, Milvus, or Weaviate), cloud infrastructure, and proficiency in languages like Python are typically expected. Strong problem-solving skills, effective communication, and the ability to work cross-functionally help engineers stand out. These competencies are vital to efficiently design, deploy, and maintain scalable vector search solutions that power modern AI applications.

What are the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
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What job categories do people searching Vector Databases jobs in Lincoln, NE look for? The top searched job categories for Vector Databases jobs in Lincoln, NE are:
Infographic showing various Vector Databases job openings in Lincoln, NE as of July 2026, with employment types broken down into 72% Full Time, and 28% Contract. Highlights an 66% In-person, and 34% Remote job distribution.

CyberSecurity AI Engineer

NELNET BUSINESS SOLUTIONS

Lincoln, NE • On-site

$115K - $155K/yr

Full-time

Medical, Dental, Vision, Life, Retirement

Posted 11 days ago


Job description

Nelnet is a diversified and innovative company committed to enriching lives through the power of service as a student loan servicer, professional services company, consumer loan originator and servicer, payments processor, renewable energy solutions, and K-12 and higher education expert. For over 40 years, Nelnet has been serving its customers, associates, and communities.

The perks of working at Nelnet go beyond our benefits package. When you join the Nelnet team, you're part of a community invested in the success of each individual. That support comes through in our work, as we are united by our mission of creating opportunities for people where they live, learn, and work.

We need someone who's equally comfortable in security engineering and AI technology, and who's ready to work closely with IT, security, and data science teams to build AI solutions that are secure, compliant, and built to last.
This role calls for real technical depth, sound judgment, and a proactive approach to risk management. Here's what you'll be doing:
- Monitoring and strengthening the security posture of our AI/ML systems, APIs, and model-serving environments
- Building detection and monitoring capabilities to identify risks such as model misuse, prompt injection, data poisoning, and unauthorized model access
- Partnering with development, operations, and security teams to secure AI environments across their full lifecycle
- Automating security monitoring and remediation workflows for AI systems
- Evaluating and implementing AI security tools and model governance solutions
- Contributing to the development of AI-specific risk frameworks, controls, and policies
- Staying current on the evolving AI risk landscape and supporting security assessments and testing of LLMs and AI services

AI and Security Engineering
Secure AI/ML model development and deployment environments, including LLMs, vector databases, and training pipelines.
Build automated tools to detect and mitigate AI-related risks, such as anomalous model outputs and prompt injection attempts.
Conduct security testing and threat modeling for AI systems.
Support logging, monitoring, and alerting for AI/ML environments integrated with SIEM/XDR platforms.
Help design model access controls, encryption, and governance enforcement.
Assist in developing and enforcing AI-related security policies and procedures.

Innovation
Question legacy assumptions and recommend AI-native security approaches.
Identify short- and long-term strategies for securing AI assets in ways that support business value.
Build security prototypes and proof-of-concepts for emerging AI architectures.
Stay current on AI security research and turn findings into practical defenses.

Agility
Adapt as the AI landscape and threat environment continue to evolve.
Navigate ambiguity in AI risk management and compliance requirements.
Help business and technical partners adjust to changing AI security requirements.
Stay responsive and adaptive when incidents involving AI systems arise.

Problem Solving
Investigate security incidents involving AI-generated outputs or manipulated inputs.
Apply critical thinking to defend against emerging AI risks.
Develop layered mitigation strategies across models, APIs, and infrastructure.
Own problem resolution from start to finish.

Collaboration
Partner with CyberSecurity analysts, developers, and AI engineers to defend AI systems.
Mentor peers on emerging risks and best practices in AI/ML security.
Foster cross-functional alignment to build AI security into enterprise roadmaps.

Communication Skills
Translate technical AI security issues into business risk language for stakeholders.
Write clear documentation for detection rules, playbooks, and findings.
Present AI risk scenarios and mitigation strategies to both technical and non-technical audiences.

Strategic Focus
Align AI security efforts with Nelnet's business, compliance, and technology goals.
Serve as a trusted advisor on AI governance, LLM access, and model risk.
Anticipate future regulatory requirements around AI usage and safety.
Deliver security solutions that balance innovation with operational integrity.

**Pay Range for this role is - $115,000 -$155,000 dependent on experience and education.

EDUCATION:
Knowledge equivalent to completing a Bachelor's degree in Computer Science or a related field.

EXPERIENCE:

  • 3 to 5 years of experience in cybersecurity, security engineering, or risk management.

  • Hands-on experience with machine learning systems, LLMs (Anthropic Claude, OpenAI, or open-source models), or AI/ML platforms such as SageMaker, Azure ML, or Vertex AI.

  • Familiarity with adversarial machine learning concepts and model risk is preferred.

  • Experience with security tools, monitoring platforms, or security automation frameworks.

  • Experience in application security, DevSecOps, or secure software development lifecycle (SDLC) is a plus.

COMPETENCIES - SKILLS/KNOWLEDGE/ABILITIES:

Needs:

  • Knowledge of security, controls, and computer technology.

  • Ability to lead and motivate others.

  • Ability to apply statistics and probability to identify problems, trends, and relationships in work-related data.

  • Knowledge of at least one computer development language, along with related methodologies and techniques.

  • Understanding of AI system architectures and their security implications.

  • Scripting or development experience in Python or a similar language.

  • Knowledge of AI-specific risk frameworks such as MITRE ATLAS or the OWASP LLM Top 10.

  • Strong analytical skills and comfort working with data, logs, and system telemetry.

  • Ability to work across teams and lead cross-functional security initiatives.

  • Familiarity with regulations governing IT environments.

  • Familiarity with regulatory and ethical frameworks around AI security and model governance.

  • Familiarity with enterprise LLM deployment and governance, including tools like Claude or similar platforms.

  • Familiarity with infrastructure deployment and systems administration.

  • Excellent organizational, presentation, verbal, and written communication skills.

  • Ability to assess and communicate risk and urgency clearly to both management and engineering staff.

  • Strong self-motivation, with the ability to set and follow through on long-term goals.

  • Genuine interest in staying technically current and building new expertise.

  • Comfortable questioning existing assumptions when it makes sense to do so.

  • Openness to changing technology and business needs.

  • Sees change as a chance to grow rather than a disruption.

  • Ability to adjust communication style to fit the audience.

Wants:

  • Knowledge of enterprise risk management and security governance frameworks.

  • Familiarity with common security tooling and methodologies.

  • Solid understanding of machine learning architectures, LLMs (GPT, LLaMA, Claude), and common AI frameworks such as PyTorch, TensorFlow, or Hugging Face.

-Please note that we are unable to provide visa sponsorship for this position. To be considered, candidates must already be authorized to work in the United States without the need for current or future sponsorship

Our benefits package includes medical, dental, vision, HSA and FSA, generous earned time off, 401K/student loan repayment, life insurance & AD&D insurance, employee assistance program, employee stock purchase program, tuition reimbursement, performance-based incentive pay, short- and long-term disability, and a robust wellness program. Click here to learn more about our benefits: Benefits & Perks - Nelnet Inc.


Nelnet is committed to providing a welcoming and respectful workplace where all associates have the opportunity to succeed. As an Equal Opportunity Employer, we ensure that all qualified applicants are considered for employment. Employment decisions are made without regard to race, color, religion/creed, national origin, gender, sex, marital status, age, disability, use of a guide dog or service animal, sexual orientation, military/veteran status, or any other status protected by federal, state, or local law. We value the unique contributions of every team member and believe that a positive work environment benefits everyone.


Qualified individuals with disabilities who require reasonable accommodations in order to apply or compete for positions at Nelnet may request such accommodations by contacting Corporate Recruiting at 402-486-5725 orcorporaterecruiting@nelnet.net.


Nelnet is a Drug Free and Tobacco Free Workplace.


Use of Artificial Intelligence in Hiring


We may use automated or artificial intelligence enabled tools to assist with the initial review of applications, such as identifying relevant skills or experience. These tools are used to support human review and do not make hiring decisions. A recruiter reviews applications and determines which candidates move forward in the hiring process. For more information, see our Privacy Policy and Pre-Use Notice: Automated Tools in Hiring