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Vector Databases Jobs in Montville, NJ (NOW HIRING)

2T Consulting

Python Agentic AI Engineer

Manhattan, NY · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

2T Consulting

Python Agentic AI Engineer

Brooklyn, NY · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

Photon

Python + Gen AI Developer - New York

Manhattan, NY · On-site

$55 - $76/hr

PostgreSQL, Vector Databases, and Advanced Retrieval strategies. ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt ...

2T Consulting

Python Agentic AI Engineer

Teaneck, NJ · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

2T Consulting

Python Agentic AI Engineer

Newark, NJ · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

2T Consulting

Python Agentic AI Engineer

Oradell, NJ · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

2T Consulting

Python Agentic AI Engineer

Orange, NJ · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

GAC Solutions

Sr AI Engineer- Full-time

Jersey City, NJ · On-site

$109K - $149K/yr

Build RAG pipelines using embeddings, chunking strategies, vector databases, semantic retrieval, reranking, response grounding, and citation patterns. * Integrate AI capabilities with AWS-hosted ...

2T Consulting

Python Agentic AI Engineer

Nutley, NJ · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

2T Consulting

Python Agentic AI Engineer

Dumont, NJ · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

2T Consulting

Python Agentic AI Engineer

Lodi, NJ

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

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Showing results 21-40

All JobsVector Databases JobsVector Databases Jobs in Montville, NJ

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 cities near Montville, NJ are hiring for Vector Databases jobs?

Cities near Montville, NJ with the most Vector Databases job openings:

Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Montville, NJ as of June 2026, with employment types broken down into 58% Full Time, 34% Part Time, 3% Temporary, 3% Contract, and 2% Nights. Highlights an 69% Physical, 3% Hybrid, and 28% Remote job distribution.

Python Agentic AI Engineer

2T Consulting

Manhattan, NY • On-site

Full-time

Posted 9 days ago

Job description

We are seeking a Python Agentic AI Engineer to design, build, and deploy scalable AI agent and backend solutions. The role combines Python, agentic AI, LLMs, RAG, AWS, APIs, microservices, and event-driven architectures to deliver production-ready AI applications.

Roles and Responsibilities
  • Design and develop autonomous and multi-agent solutions using Google ADK or Agent SDK.
  • Build agent workflows, tool calling, orchestration, and context management capabilities.
  • Integrate Gemini/OpenAI models using prompt engineering, function calling, structured outputs, grounding, and evaluation techniques.
  • Develop RAG solutions using embeddings and vector databases.
  • Build and integrate REST APIs, microservices, and event-driven architectures.
  • Develop scalable backend applications using Python, Django, GraphQL, and PostgreSQL.
  • Deploy and manage cloud-native applications on AWS.
  • Support data pipelines/ETL, application security, scalability, and resilience.
  • Implement AI observability, evaluation, security, governance, and guardrails.
  • Apply Domain-Driven Design (DDD), agile methodologies, testing, and code-review best practices.
  • Collaborate with product, engineering, data science, design, and business teams.
Required Skills & Experience
  • 7+ years of professional software engineering/system architecture experience.
  • Strong expertise in Python, particularly Django and GraphQL.
  • Hands-on experience with AI agent development using Google ADK or Agent SDK.
  • Strong experience with LLM integration, prompt engineering, and agent orchestration.
  • Experience with Gemini/OpenAI models, tool calling, function calling, structured outputs, and grounding.
  • Hands-on experience building RAG applications, vector databases, and embeddings.
  • Strong experience with AWS cloud-native application development.
  • Experience with REST APIs, microservices, SOA, and event-driven architectures.
  • Experience with PostgreSQL and data pipelines/ETL.
  • Knowledge of AI observability, evaluation, security, governance, and guardrails.
  • Strong understanding of scalable, resilient, and production-ready system architecture.
  • Strong collaboration and communication skills.
Preferred Skills
  • React and Apollo GraphQL
  • Domain-Driven Design (DDD)
  • Agile methodologies
  • AI evaluation frameworks
  • CI/CD and automated testing
  • Experience working in collaborative, in-person engineering environments

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