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Vector Databases Jobs in Delaware (NOW HIRING)

Photon

Lead AI Engineer | Onsite - Delaware

Wilmington, DE · On-site

$99K - $131K/yr

Own delivery of RAG systems , including vector database selection/topology and knowledgebase design . * Drive delivery of AI agent and multi-agent systems and tool-use/MCP integration patterns. * Own ...

Morson Talent

AI Adoption Specialist

Wilmington, DE · On-site +1

$35 - $45/hr

Exposure to RAG concepts or vector databases. Awareness of Agile, Lean, or XP delivery methodologies. Ability to identify highvalue AI use cases and support ideation and scoping. Coursework ...

Photon

Lead AI Engineer

Newark, DE · On-site

$100K - $132K/yr

Own delivery of RAG systems , including vector database selection/topology and knowledgebase design . * Drive delivery of AI agent and multi-agent systems and tool-use/MCP integration patterns. * Own ...

Berkley

Principal Engineer - AI Platform

Wilmington, DE · On-site

$131K - $175K/yr

Excellent knowledge of modern Vector and Graph databases * Excellent knowledge of Redis cache or other similar solutions * Experience with version control systems like Git and GitLab and Agile ...

Berkley

Principal Engineer - AI Platform

Wilmington, DE

$131K - $175K/yr

Excellent knowledge of modern Vector and Graph databases * Excellent knowledge of Redis cache or other similar solutions * Experience with version control systems like Git and GitLab and Agile ...

Berkley

Principal Engineer - AI Platform

Wilmington, DE

$131K - $175K/yr

Excellent knowledge of modern Vector and Graph databases * Excellent knowledge of Redis cache or other similar solutions * Experience with version control systems like Git and GitLab and Agile ...

Agilent

Head of Data Engineering, AI CoE

Wilmington, DE

$171K - $321K/yr

Lakehouse, vector, and graph retrieval foundations underpinning grounded agent behavior. * Agentic workloads that build the Fabric itself: metadata generation, entity resolution, quality scoring, and ...

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Vector Databases information

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 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 popular job titles related to Vector Databases jobs in Delaware? For Vector Databases jobs in Delaware, the most frequently searched job titles are:
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What cities in Delaware are hiring for Vector Databases jobs? Cities in Delaware with the most Vector Databases job openings:
Vector Databases jobs near you

Technical Lead - Operation AI/ML Enablement

Photon

Newark, DE • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Owns end-to-end delivery of how the organization builds LLM-powered applications: SDK/integration architecture, retrieval and agent design, guardrails, and evaluation. Makes the calls on which LLMs to use for which use cases and sets the observability/cost discipline around AI systems, but is measured on shipped outcomes runs the offshore team day-to-day and stays hands-on to unblock delivery risk.
Description for Internal Candidates
Key Responsibilities
Own delivery of LLM API integration and SDK patterns used across applications.
Set organizational guidance on which LLM to use for what use case and drive delivery of multi-LLM scenarios.
Define standards for advanced prompt engineering and context window management.
Own delivery of RAG systems, including vector database selection/topology and knowledgebase design.
Drive delivery of AI agent and multi-agent systems and tool-use/MCP integration patterns.
Own guardrails delivery (safety, compliance, PII handling in prompts/outputs) critical in a financial-services context.
Define evaluation frameworks and real-time eval strategy; set standards for AI testing in CI/CD.
Own latency profiling, AI observability, and cost tracking/management for LLM-backed systems.
Run day-to-day delivery of the offshore development team: sprint commitments, code/design review, real-time unblocking, and hands-on work on critical-path AI features.
Report delivery status, risks, and blockers to engineering leadership.
Must-Have Qualifications
6+ years in software engineering, with 2+ years as a tech lead owning end-to-end delivery of LLM/AI-powered systems (not a pure design/review architect role).
Proven track record of shipping AI-powered features on committed timelines, including hands-on troubleshooting under delivery pressure.
Strong, hands-on Python skills at an architectural/systems level.
Proven experience architecting LLM API integrations and SDK-level abstractions across multiple providers.
Demonstrated judgment on model selection (cost, latency, capability trade-offs) across use cases.
Deep expertise in prompt engineering and context window management at scale.
Proven design experience with RAG systems, including vector database architecture and knowledgebase design.
Experience architecting AI agents/multi-agent systems and tool-use patterns (MCP or equivalent).
Strong understanding of guardrails design content safety, PII protection, compliance controls for AI outputs.
Experience defining evaluation frameworks and integrating AI testing into CI/CD.
Proven ability to design for latency, observability, and cost management of AI systems in production.
Financial-services or regulated-industry experience strongly preferred given compliance/guardrail stakes.
Strong stakeholder communication; able to directly manage day-to-day delivery of an offshore team (standups, unblocking, sprint accountability).
Nice-to-Have Qualifications
Direct experience with specific frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent).
Experience with AWS Bedrock or comparable managed LLM platforms.
Contributions to or deep familiarity with MCP (Model Context Protocol) implementations.
Experience building internal LLM gateways.
Familiarity with responsible-AI/model-risk-management frameworks used in financial services.

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