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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 ...

Eisneramper Llp

AI Solution Architect

Stamford, CT · On-site +1

$67.25 - $88.50/hr

Vector Databases: Azure Cosmos DB, Pinecone, or Weaviate * DevOps & MLOps: Azure DevOps, GitHub Actions, Docker, Kubernetes About EisnerAmper: EisnerAmper is one of the largest accounting, tax, and ...

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, 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:

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
Infographic showing various Pinecone Vector Databases job openings in Connecticut as of June 2026, with employment types broken down into 63% Full Time, 25% Part Time, 3% Temporary, 6% Contract, and 3% Nights. Highlights an 58% Physical, 11% Hybrid, and 31% Remote job distribution.

SR. ML Engineer with Java and Spring Boot

TekCommands Inc

Hartford, CT • On-site

$126K - $165K/yr

Contractor

Posted 15 days ago

Job description

Detailed JD:

Required Skills and Experience

• 5+ years of experience as a Software Engineer or Java Developer in enterprise environments.

• Strong experience with Java and Spring Boot.

• Experience with Python and PySpark for data engineering and AI/ML workloads.

• Strong API development experience (REST required).

• Experience designing, developing, or integrating AI/ML solutions in production environments.

• Knowledge of Generative AI technologies, including LLMs, prompt engineering, RAG architecture, vector databases, and AI agent frameworks.

• Experience consuming AI services through APIs (OpenAI, Azure OpenAI, Vertex AI, Anthropic, or similar platforms).

• Familiarity with MLOps concepts, model deployment, monitoring, and governance best practices.

• Exposure to AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, or similar technologies.

• Strong problem-solving skills and ability to work effectively within a fast-paced Agile environment.

Preferred Qualifications

• Experience working with relational databases (DB2 preferred).

• Experience building enterprise AI applications using LangChain, Semantic Kernel, LlamaIndex, or similar frameworks.

• Experience implementing AI governance, security, and responsible AI practices.

• Familiarity with vector databases such as Pinecone, Weaviate, Chroma, or Vertex AI Vector Search.

• Experience modernizing legacy systems and integrating AI-driven business processes.

• Knowledge of financial services, commissions, brokerage, or enterprise compensation platforms.