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Pinecone Vector Databases Jobs in Connecticut (NOW HIRING)

Kanak Elite Services Inc

AI Developer

Conantville, CT · On-site

Knowledge of LLMs, embeddings, and vector databases (Pinecone, FAISS, etc.). * Understanding of SQL/NoSQL databases and data integration techniques. * Strong problem-solving and communication skills.

Franklin Templeton

AI/ML Lead Engineer

Stamford, CT · On-site

$109K - $143K/yr

Hands-on experience with vector databases (e.g., Pinecone, FAISS), RAG architectures, and data grounding techniques. * Production reliability and monitoring: Experience implementing observability ...

Franklintempleton

AI/ML Lead Engineer

Stamford, CT

$109K - $143K/yr

Hands-on experience with vector databases (e.g., Pinecone, FAISS), RAG architectures, and data grounding techniques. * Production reliability and monitoring: Experience implementing observability ...

Gartner

Sr. Data Scientist

Stamford, CT

... vector databases (e.g., Pinecone, Weaviate), distributed machine learning (spark), Linux and shell scripting * Experience with cloud computing services such as AWS or Azure ML * Strong ability to ...

Gartner

Sr. Data Scientist

Stamford, CT · On-site

... vector databases (e.g., Pinecone, Weaviate), distributed machine learning (spark), Linux and shell scripting * Experience with cloud computing services such as AWS or Azure ML * Strong ability to ...

Gartner, Inc.

Sr Data Scientist

Stamford, CT · On-site

... vector databases (e.g., Pinecone, Weaviate), distributed machine learning (Spark), AI evals and observability solutions * Working experience in some of the following AI and data science areas:

Gartner

Sr Data Scientist

Stamford, CT · On-site

... vector databases (e.g., Pinecone, Weaviate), distributed machine learning (Spark), AI evals and observability solutions * Working experience in some of the following AI and data science areas:

Gartner

Sr Data Scientist

Stamford, CT

... vector databases (e.g., Pinecone, Weaviate), distributed machine learning (Spark), AI evals and observability solutions * Working experience in some of the following AI and data science areas:

Gartner

Director, Data Science

Stamford, CT · On-site

... vector databases (Pinecone, Weaviate), distributed ML (Spark), Linux/shell scripting, and cloud platforms (AWS SageMaker, Azure ML). * Working experience in: Large Language Models/Generative AI ...

Gartner

Director, Data Science

Stamford, CT · Remote

... vector databases (Pinecone, Weaviate), distributed ML (Spark), Linux/shell scripting, and cloud platforms (AWS SageMaker, Azure ML). * Working experience in: Large Language Models/Generative AI ...

Gartner, Inc.

Director, Data Science

Stamford, CT · On-site

... vector databases (Pinecone, Weaviate), distributed ML (Spark), Linux/shell scripting, and cloud platforms (AWS SageMaker, Azure ML). * Working experience in: Large Language Models/Generative AI ...

JobTarget

Agentic AI Developer Intern

Stamford, CT · On-site

$27 - $30/hr

Vector databases such as Pinecone, Weaviate, Chroma, FAISS, or pgvector * Retrieval-augmented generation, embeddings, prompt engineering, or model evaluation * FastAPI, Flask, Node.js, React, or ...

All JobsPinecone Vector Databases JobsPinecone Vector Databases Jobs in Connecticut

Pinecone Vector Databases information

What is a Pinecone Vector Database?

A Pinecone Vector Database is a cloud-based service designed to efficiently store, index, and search high-dimensional vector data, such as embeddings generated by machine learning models. It enables fast similarity search, making it ideal for use cases like semantic search, recommendation systems, and AI-powered applications. Pinecone handles the complexity of scaling and managing vector data, so developers can focus on building intelligent applications without worrying about infrastructure.

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

To thrive as a Pinecone Vector Database Engineer, you need a strong background in computer science, data engineering, and experience with large-scale distributed systems, often supported by a relevant degree or equivalent experience. Proficiency in Python, REST APIs, cloud platforms (AWS, GCP), and vector search technologies, along with familiarity with Pinecone’s SDK and database management, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and deliver scalable solutions. These skills ensure robust database performance, efficient data retrieval, and successful integration of vector search capabilities into real-world applications.

What are some common challenges faced by engineers working with Pinecone Vector Databases, and how can they be addressed?

Engineers working with Pinecone Vector Databases often encounter challenges such as optimizing vector search performance at scale, ensuring data consistency across distributed systems, and integrating the database with various machine learning pipelines. Addressing these challenges typically involves tuning indexing parameters, monitoring resource utilization, and collaborating closely with data scientists to understand retrieval requirements. Regularly reviewing documentation and participating in community forums can also help engineers stay current with best practices and new features.

What is the difference between Pinecone Vector Databases vs Data Engineers?

AspectPinecone Vector DatabasesData Engineers
Primary RoleManaging and deploying vector database solutions for AI/ML applicationsDesigning, building, and maintaining data pipelines and infrastructure
Skills & CertificationsKnowledge of vector databases, cloud platforms, programming (Python, SQL)Data modeling, ETL processes, cloud services, programming (Python, Java)
Work EnvironmentTech companies, AI startups, cloud providersData-driven organizations, tech firms, finance, healthcare

While Pinecone Vector Databases specialists focus on deploying and managing vector database solutions for AI applications, Data Engineers build and maintain the data infrastructure that supports these systems. Both roles require programming skills and familiarity with cloud platforms, but their core responsibilities differ: one centers on database management, the other on data pipeline development.

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

AI Developer

Kanak Elite Services Inc

Conantville, CT • On-site

Contractor

Re-posted 17 days ago

Job description

Hello There,

My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of AI Developer with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .

Job Description

Position           : AI Developer
Location          : Greenwich, CT Hybrid (Need local candidate)
Duration         : 6+ Months
Interview        : ONSITE INTERVIEW IS A MUST

Description

  • The AI Developer will develop, and implement AI-based solutions that support clients digital and data initiatives. The role involves hands-on development using AWS services (such as Amazon Bedrock, SageMaker, Lambda, and S3) and AI technologies including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering.
  • You will collaborate with data engineering and application development teams to build intelligent, scalable, and secure AI solutions integrated into enterprise systems

Key Responsibilities

  • Develop and maintain AI and machine learning models using AWS Bedrock, SageMaker, and Python-based frameworks.
  • Good to have experience on building AI Agents.
  • Implement and optimize data pipelines, embeddings, and vector search integrations for RAG-based applications.
  • Support deployment, monitoring, and lifecycle management of AI models following MLOps best practices.
  • Work closely with cross-functional teams to integrate AI components into applications and workflows.
  • Assist in evaluating and adopting emerging AI tools and frameworks to improve system performance.
  • Maintain code quality, documentation, and adherence to security and scalability standards.

Qualifications

  • Bachelor’s degree in Computer Science, Data Science, Engineering or related field.
  • 3–5 years of experience in software or AI development.
  • Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or Hugging Face.
  • Hands-on experience with AWS cloud services (Bedrock, SageMaker, Lambda, API Gateway).
  • Knowledge of LLMs, embeddings, and vector databases (Pinecone, FAISS, etc.).
  • Understanding of SQL/NoSQL databases and data integration techniques.
  • Strong problem-solving and communication skills.