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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

Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers Key Initiatives this role will support Advisor-Facing AI * Design and implement agents ...

Franklintempleton

AI/ML Lead Engineer

Stamford, CT · On-site

$109K - $143K/yr

Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers Key Initiatives this role will support Advisor-Facing AI * Design and implement agents ...

Imea

AI/ML Lead Engineer

Stamford, CT · On-site

$180 - $212/hr

Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers Key Initiatives this role will support Advisor-Facing AI * Design and implement agents ...

Info Way Solutions

Gen AI Engineer

Hartford, CT · On-site

... vector databases, and retrieval-augmented generation (RAG). • Strong understanding of NLP, deep learning, and model fine-tuning techniques. • Experience working with MLOps, cloud-based AI ...

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:

Gartner

Director, Data Science

Stamford, CT · Remote

Proficiency with Python, ML frameworks (scikit-learn, NLTK, PyTorch, TensorFlow, Hugging Face, LangChain), SQL/relational databases (Oracle), NoSQL/graph databases (MongoDB), vector databases ...

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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 Connecticut?

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

What job categories do people searching Vector Databases jobs in Connecticut look for?

The top searched job categories for Vector Databases jobs in Connecticut are:

What cities in Connecticut are hiring for Vector Databases jobs?

Cities in Connecticut with the most Vector Databases job openings:

Vector Databases jobs near you

SR. ML Engineer with Java and Spring Boot

Hartford, CT • On-site

TekCommands Inc
11 - 50 employees
Learn more about TekCommands Incexternal

$126K - $165K/yr

Contractor

Posted 13 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.