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Pinecone Vector Databases Jobs in Kentucky (NOW HIRING)

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.

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Infographic showing various Pinecone Vector Databases job openings in Kentucky as of August 2026, with employment types broken down into 87% Full Time, 5% Part Time, 1% Temporary, and 7% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 23 days ago


Cognizant rating

7.4

Company rating: 7.4 out of 10

Based on 85 frontline employees who took The Breakroom Quiz

52nd of 72 rated business consultants


Job description

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About the Role
As an Agentic AI Engineer, you will make an impact by designing, developing, and deploying advanced AI agents and agentic systems that leverage Large Language Models (LLMs) to solve complex business challenges. You will be a valued member of our AI Engineering team and work collaboratively with data scientists, machine learning engineers, product managers, architects, and business stakeholders to deliver innovative AI-driven solutions.
Candidate must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
In This Role, You Will:
  • Design and develop autonomous AI agents capable of reasoning, planning, and executing complex multi-step tasks using leading LLM technologies.
  • Build and orchestrate agentic workflows and multi-agent systems using LangGraph, enabling stateful execution, memory management, and agent collaboration.
  • Develop Retrieval-Augmented Generation (RAG), tool-calling, and function-calling solutions using LangChain and related frameworks.
  • Architect and integrate AI agents with enterprise systems, APIs, databases, and vector stores such as Pinecone, Chroma, Weaviate, and FAISS.
  • Implement memory frameworks including short-term, long-term, semantic, and episodic memory for intelligent agent behavior.
  • Design prompt engineering strategies and optimize LLM performance for accuracy, reliability, scalability, and cost efficiency.
  • Develop guardrails, validation layers, and human-in-the-loop workflows to ensure safe and reliable AI solutions.
  • Create and maintain evaluation frameworks to assess agent effectiveness, hallucination rates, and task completion metrics.
  • Deploy AI applications to production environments using AWS, Azure, or GCP, leveraging Docker, Kubernetes, and CI/CD pipelines.
  • Monitor, troubleshoot, and optimize production AI systems for performance, latency, scalability, and token utilization.
  • Collaborate with cross-functional teams to translate business requirements into innovative AI-powered solutions.
  • Stay current with emerging developments in agentic AI, LLMs, frameworks, and industry best practices.
Work Model
This is an onsite position based in Louisville, Kentucky, requiring attendance at the client or Cognizant office 5 days per week. Candidates should be comfortable working in a collaborative, office-based environment and partnering closely with cross-functional teams and stakeholders.
The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.
What You Need to Have to Be Considered
  • 8+ years of experience in software engineering, AI engineering, machine learning, or related technology roles.
  • Strong experience building solutions using Large Language Models (LLMs), Generative AI, and Agentic AI frameworks.
  • Hands-on expertise with LangChain and LangGraph for developing agentic workflows and multi-agent solutions.
  • Strong programming experience in Python and modern software development practices.
  • Experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
  • Experience integrating AI solutions with APIs, databases, enterprise applications, and vector databases.
  • Experience deploying applications in cloud environments such as AWS, Azure, or GCP.
  • Familiarity with Docker, Kubernetes, CI/CD pipelines, and production-grade application deployment.
  • Strong analytical, problem-solving, and collaboration skills.
These Will Help You Stand Out
  • Experience with multi-agent architectures and agent-to-agent communication frameworks.
  • Experience implementing memory management strategies for AI agents.
  • Knowledge of Responsible AI, AI governance, and AI safety best practices.
  • Experience evaluating and optimizing LLM outputs, token consumption, latency, and overall cost.
  • Familiarity with MLOps and AI application monitoring frameworks.
  • Experience working with open-source LLMs and emerging agentic AI technologies.
Salary and Other Compensation
The annual salary for this position is between depends on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of Cognizant's applicable plans.
Benefits
Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
  • Medical/Dental/Vision/Life Insurance
  • Paid holidays plus Paid Time Off
  • 401(k) plan and contributions
  • Long-term/Short-term Disability
  • Paid Parental Leave
  • Employee Stock Purchase Plan
Disclaimer
The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization's unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email [email protected] for roles based in the Americas or [email protected] for roles based in India.

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