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Vector Development Jobs in Washington (NOW HIRING)

LCG, Inc.

Full Stack Developer, Associate

Bethesda, MD · Hybrid

Support development of RAG pipeline components integrating vector databases with enterprise data sources. * Assist in developing LLM-integrated applications and APIs that connect AI services with ...

Purple Drive

Enterprise Architect

Mclean, VA · On-site

$70.75 - $91.25/hr

Experience with vector databases and cloud deployments. AI Engineer * Develop AI solutions, including requirements definition, design, cloud-native development, deployment, and integration.

Prophecy Technologies

Gen AI Solution Architect

Mclean, VA · On-site

$63.75 - $84/hr

The Gen AI Solution Architect will lead the design and development of enterprise-grade AI solutions ... Design AI solutions using vector databases for semantic search and RAG (Retrieval-Augmented ...

Technogen

Big Data Consultant

Rockville, MD

We have offices in VA,MD & Offshore development centers in India.We have successfully excuted 100 ... Understand Call Center features; such as, Automatic Call Distributions (ACD), Call Vectors, and ...

Technogen

Big Data Consultant

Rockville, MD · On-site

We have offices in VA,MD & Offshore development centers in India.We have successfully excuted 100 ... Vectors, and Vector Directory numbers (VDN). • Ability to develop work and call flows • ...

Technology Ventures

Senior GenAI Engineer

Reston, VA · On-site

$108K - $149K/yr

The ideal candidate will have strong expertise in Python-based backend development, LLM -powered applications, cloud-native deployment, vector databases, and modern DevOps practices . This role ...

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Vector Development information

What is the difference between Vector Development vs Data Analyst?

AspectVector DevelopmentData Analyst
Required CredentialsBachelor's in Computer Science, Engineering, or related fields; programming skillsBachelor's in Statistics, Mathematics, or related fields; analytical skills
Work EnvironmentTech companies, R&D labs, software development teamsBusiness, finance, healthcare, and marketing sectors
Industry UsageAI, machine learning, computer vision, roboticsData interpretation, reporting, business insights

Vector Development involves creating algorithms and models for AI and machine learning applications, often requiring programming expertise. Data Analysts focus on interpreting data, generating reports, and providing insights to support business decisions. While both roles work with data and require analytical skills, Vector Developers are more technical and programming-oriented, whereas Data Analysts emphasize data interpretation and communication.

What are popular job titles related to Vector Development jobs in Washington? For Vector Development jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Vector Development jobs in Washington look for? The top searched job categories for Vector Development jobs in Washington are:
Vector Development jobs near you

Senior AI Solution Architect

Interon IT Solutions

Chantilly, VA • Remote

Contractor

Posted 14 days ago

Key responsibilities

  • Lead the architecture, design, and implementation of a scalable Agentic AI platform for enterprise use cases.

  • Design AI-driven solutions focused on knowledge discovery, reasoning, orchestration, and enterprise automation.

  • Build and guide the development of reasoning and orchestration layers using Agentic AI frameworks.

Job description

#W2 Role
 
Role: Senior AI Solution Architect
Location: Remote
Experience: 10+ years preferred
Mandatory Skills: GCP, Vector Database, Agentic AI Frameworks, Python
 
Job Description
We are looking for a highly experienced and hands-on Senior AI Solution Architect to lead the design, development, and deployment of a scalable Agentic AI platform focused on knowledge-driven reasoning, enterprise transformation, and intelligent automation.
The ideal candidate should have strong experience in cloud modernization, AI architecture, enterprise API transformation, vector databases, Python development, and GCP-based AI/ML solutions. This role requires someone who can work closely with business, architecture, engineering, data, and cloud teams to transform enterprise services into AI-driven, searchable, and reusable capabilities through Knowledge Record Management, reasoning layers, orchestration frameworks, and agentic workflows.
 
Key Responsibilities
Lead the architecture, design, and implementation of a scalable Agentic AI platform for enterprise use cases.
Design AI-driven solutions focused on knowledge discovery, reasoning, orchestration, and enterprise automation.
Build and guide the development of reasoning and orchestration layers using Agentic AI frameworks.
Transform enterprise services, documents, and business knowledge into searchable APIs and reusable AI capabilities.
Design and implement Knowledge Record Management patterns to structure, classify, retrieve, and govern enterprise knowledge.
Build RAG-based and agentic workflows using vector databases, embeddings, retrieval pipelines, and LLM orchestration.
Develop AI solution architecture on Google Cloud Platform (GCP) using cloud-native services and scalable deployment models.
Lead cloud modernization and migration strategies to enable AI-first enterprise platforms.
Design secure, scalable, and reusable APIs to expose enterprise knowledge and services to AI agents.
Provide hands-on technical leadership in Python-based AI engineering, orchestration, and backend service development.
Evaluate and recommend AI frameworks, vector database platforms, embedding models, and orchestration patterns.
Partner with product owners, business stakeholders, architects, and engineering teams to convert business needs into AI solution designs.
Create architecture diagrams, technical design documents, API specifications, integration patterns, and implementation roadmaps.
Define best practices for responsible AI, security, access control, data privacy, observability, and governance.
Support proof-of-concepts, MVPs, production deployments, performance tuning, and platform scaling.
Mentor engineering teams on Agentic AI architecture, cloud-native design, and enterprise AI delivery practices.
 
Required Skills
10+ years of overall IT experience with strong experience in solution architecture, cloud architecture, AI/ML engineering, or enterprise application modernization.
Strong hands-on experience with Google Cloud Platform (GCP).
Strong experience designing and implementing Agentic AI frameworks and multi-agent workflows.
Hands-on experience with Python for AI engineering, backend services, orchestration, and API development.
Strong experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, Vertex AI Vector Search, or similar platforms.
Experience building embeddings, semantic search, RAG pipelines, reasoning workflows, and knowledge retrieval systems.
Strong understanding of LLM-based solution design, prompt engineering, tool calling, function calling, and agent orchestration.
Experience building AI-driven APIs and transforming enterprise services into searchable, consumable, and reusable AI capabilities.
Strong understanding of cloud modernization, migration patterns, microservices, APIs, and enterprise integration.
Experience designing scalable, secure, and production-ready AI platforms.
Strong knowledge of data pipelines, metadata, document ingestion, chunking strategies, indexing, retrieval optimization, and knowledge governance.
Ability to define architecture standards, reusable design patterns, and technical implementation roadmaps.
Strong stakeholder management, communication, documentation, and leadership skills.
 
Preferred Skills
Experience with GCP services such as Vertex AI, BigQuery, Cloud Run, Cloud Functions, Cloud Storage, Pub/Sub, Cloud SQL, GKE, IAM, and API Gateway.
Experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar tools.
Experience with Knowledge Graphs, ontology design, metadata management, taxonomy design, or enterprise knowledge management.
Experience designing reasoning/orchestration layers for enterprise GenAI platforms.
Experience with API management, REST APIs, GraphQL, event-driven architecture, and microservices.
Experience with MLOps, CI/CD, Docker, Kubernetes, Terraform, and cloud deployment automation.
Knowledge of security, privacy, governance, and compliance requirements for enterprise AI platforms.
 
Nice to Have
Experience with cloud migration and modernization from legacy platforms to GCP.
Experience implementing AI platforms for enterprise search, knowledge assistants, service automation, or intelligent workflow orchestration.
Exposure to model evaluation, LLM observability, hallucination controls, guardrails, and prompt/version management.
Experience working in financial services, healthcare, insurance, retail, or large enterprise environments.
 
Certifications
GCP Professional Cloud Architect, Professional Machine Learning Engineer, or related cloud/AI certifications are a plus.