ZipRecruiter

Log In

1

Vector Databases Jobs in Washington, DC (NOW HIRING)

Leidos

Agentic AI Developer

Chantilly, VA · On-site

$69K - $125K/yr

... vector databases, and modern backend architectures. • Strong understanding of prompt engineering, RAG pipelines, and agent orchestration concepts. • Strong communication and problem-solving ...

Leidos

Agentic AI Developer

Chantilly, VA · On-site

$69K - $125K/yr

Familiarity with REST APIs, vector databases, and modern backend architectures. Strong understanding of prompt engineering, RAG pipelines, and agent orchestration concepts. Strong communication and ...

Leidos

Agentic AI Developer

Columbia, MD · On-site

$69K - $125K/yr

Familiarity with REST APIs, vector databases, and modern backend architectures. Strong understanding of prompt engineering, RAG pipelines, and agent orchestration concepts. Strong communication and ...

Accenture Federal Services

AI Architect

Chantilly, VA · On-site

Experience with Python, agent frameworks, data engineering, APIs/microservices, vector databases, SQL engines, distributed systems, cloud services, RAG * Understanding of DevOps tools and principals ...

1 point system

AI/ML Engineer

Reston, VA · On-site

Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM‑powered automation (chatbots, summarization, semantic search, anomaly detection). * Implement CI/CD pipelines ...

Tata Consultancy Service Limited

Engineer

Mclean, VA · On-site

$100K - $120K/yr

Create end-to-end GenAI workflows using vector databases, embeddings, and cloud-based model orchestration. • Integrate AI into Products : Work with APIs, microservices, and backend systems to embed ...

Tech Tammina LLC

AI/ML Engineer

Reston, VA · On-site

Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM‑powered automation (chatbots, summarization, semantic search, anomaly detection). Implement CI/CD pipelines (GitHub ...

The Josef Group

Senior Data Scientist

Columbia, MD · On-site

Hands-on experience with SQL, NoSQL, graph (Neo4j), and vector databases * Experience working in AWS and/or Azure cloud environments Preferred Qualifications: * Experience with Hadoop, Spark, Kafka ...

previous page

Showing results 41-60

All JobsVector Databases JobsVector Databases Jobs in Washington, DC

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 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 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 popular job titles related to Vector Databases jobs in Washington, DC?

For Vector Databases jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Washington, DC look for?

The top searched job categories for Vector Databases jobs in Washington, DC are:

Vector Databases jobs near you

Principal AI Solutions Architect

Digital Links Inc

Washington, DC • On-site

Contractor

Re-posted 25 days ago

Job description

Job Description:

Job Title: Principal AI Solutions Architect – Cloud & Enterprise AI 
Location: 95% Remote – Occasional Onsite Meetings in Washington, D.C.
 
Position Overview
Patient-Centered Outcomes Research Institute is seeking a highly experienced Principal AI Solutions Architect to lead the design and implementation of secure, scalable, and enterprise-grade AI and cloud solutions supporting healthcare research, data modernization, and digital transformation initiatives.
This role requires deep expertise in AWS cloud architecture, Generative AI ecosystems, enterprise systems integration, MLOps, AI governance, and cloud-native application design. The ideal candidate will serve as a strategic technical leader responsible for architecting AI-enabled platforms while ensuring security, compliance, governance, and operational excellence.
The architect will collaborate closely with enterprise leadership, data engineering teams, cybersecurity, DevSecOps, and application development teams to drive innovation using modern AI technologies including LLMs, RAG architectures, MCP (Model Context Protocol), AI Guardrails, and enterprise AI orchestration frameworks.
 
Key Responsibilities
Design and implement enterprise-scale AWS cloud architectures supporting AI/ML and GenAI workloads.
Architect scalable, resilient, and secure cloud-native solutions using:
Microservices
Containers
APIs
Event-driven architectures
Serverless computing
Lead architecture and integration efforts for:
Generative AI platforms
LLM orchestration
AI agents
RAG pipelines
Vector databases
Knowledge graph integrations
Design and implement AI governance frameworks including:
Responsible AI
AI Guardrails
Model monitoring
Risk management
Compliance controls
Implement and support MCP (Model Context Protocol) integrations and AI interoperability solutions.
Architect enterprise MLOps pipelines for:
Model training
Validation
Deployment
Observability
Lifecycle management
Design secure AI solutions leveraging AWS services such as:
SageMaker
Bedrock
Lambda
ECS/EKS
API Gateway
DynamoDB
S3
IAM
CloudWatch
Collaborate with DevSecOps teams to implement:
CI/CD pipelines
Infrastructure as Code (Terraform/CloudFormation)
Security automation
Compliance scanning
Develop architecture standards, governance models, and technical roadmaps.
Provide technical leadership, mentoring, and architectural guidance across engineering teams.
Evaluate emerging AI technologies and recommend enterprise adoption strategies.
Support enterprise modernization initiatives aligned with healthcare and research data platforms.
 
Required Qualifications
10+ years of experience in:
Enterprise Architecture
Cloud Engineering
Systems Architecture
Solution Architecture
5+ years of hands-on AWS cloud architecture experience.
 
Strong expertise in:
AI/ML architecture
Generative AI
Large Language Models (LLMs)
AI orchestration frameworks
MLOps
 
Experience with:
MCP (Model Context Protocol)
AI Guardrails
Responsible AI frameworks
Strong experience designing:
Distributed systems
Enterprise integrations
Cloud-native platforms
 
Expertise in:
API architecture
Event-driven systems
Kubernetes
Docker
Serverless computing
Experience with Infrastructure as Code:
Terraform
CloudFormation
Strong DevSecOps and CI/CD implementation experience.
Strong understanding of:
Security architecture
Governance
Compliance
Enterprise cloud controls
Strong communication and stakeholder management skills.
 
Preferred Qualifications:
AWS Certified Solutions Architect – Professional
AWS AI/ML Specialty Certification
Experience with:
LangChain
OpenAI APIs
Bedrock Agents
Vector databases
Knowledge Graphs
RAG architectures
Experience supporting healthcare, research, nonprofit, or federal organizations preferred.
Experience implementing enterprise AI governance and Responsible AI initiatives.
Familiarity with healthcare data modernization and interoperability standards is a plus.
 
Technical Skills:
AWS Cloud Architecture
Generative AI / GenAI
LLM Integration
MCP (Model Context Protocol)
AI Guardrails
Responsible AI
MLOps
Terraform / CloudFormation
Kubernetes / Docker
Python
API Design
DevSecOps
CI/CD Pipelines
Enterprise Systems Integration
Data & AI Governance
RAG Architectures
Vector Databases
Bedrock / SageMaker
 
Work Environment
95% Remote
Occasional onsite meetings in Washington, D.C.
Collaborative enterprise architecture and innovation environment
Opportunity to lead next-generation AI transformation initiatives