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Vector Databases Jobs in Athens, AL (NOW HIRING)

Parsons Corporation

AI Engineer

Huntsville, AL · On-site

$134K - $241K/yr

Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses. * Exposure to model hosting, inference pipelines, or cloud-based AI development ...

Parsons Corporation

AI Engineer

Huntsville, AL · On-site

$134K - $241K/yr

Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses. * Exposure to model hosting, inference pipelines, or cloud-based AI development ...

Parsons

AI Engineer

Huntsville, AL · On-site

$134K - $241K/yr

Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses. * Exposure to model hosting, inference pipelines, or cloud-based AI development ...

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All JobsVector Databases JobsVector Databases Jobs in Athens, AL

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 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 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 cities near Athens, AL are hiring for Vector Databases jobs? Cities near Athens, AL with the most Vector Databases job openings:
Vector Databases jobs near you
SOFTWARE DEVELOPER/ENGINEER (CYBER)

SOFTWARE DEVELOPER/ENGINEER (CYBER)

Quantum Research International

Huntsville, AL • On-site

Full-time

Posted 9 days ago

Job description

Job Summary:
Quantum Research International, Inc. provides services and products in various areas including cybersecurity and information operations. They are seeking an Intermediate Software Developer/Engineer to support the development of a Retrieval-Augmented Generation application leveraging Large Language Models for intelligent inference and response generation.
Responsibilities:
• Design, develop, and maintain RAG pipelines integrating vector databases, embeddings, and LLMs for accurate, context-aware inference.
• Implement and optimize retrieval logic, prompt engineering, and post-processing of LLM outputs.
• Build and integrate backend services using .NET/C#, Go, and/or Python.
• Collaborate with AI/ML engineers, data scientists, and stakeholders to translate requirements into production-grade features
• Ensure application security, performance, scalability, and compliance with government standards.
• Troubleshoot and optimize AI-assisted workflows, including latency, accuracy, and cost considerations.
• Participate in code reviews, testing, and documentation in an Agile environment.
Qualifications:
Required:
• Active TS or ability to obtain TS/SCI clearance
• At least 3+ years of professional software development experience
• Strong experience in at least two of the following: .NET/C#, Go (Golang), Python. Solid understanding of all languages listed is a plus.
• Familiarity at the developer level with AI/ML fundamentals, including embeddings and vector searching, RAG patterns, LLM inference (prompting, context windows, token management), and basic evaluation metrics for generative AI.
• Experience building RESTful APIs, microservices, or backend systems.
• Experience with non-frontier (open) AI models running in local or self-hosted environments.
Preferred:
• Direct experience developing and deploying AI-assisted applications (chatbots, knowledge assistants, document intelligence tools)
• Hands-on work with RAG frameworks/tools such as LangChain, LlamaIndex or similar.
• Experience implementing vector databases such as Pinecone, Weaviate, Chroma, pgvector, etc.
• Familarity with Python data/AI ecosystem (NumPy, Pandas, Hugging Face, sentence-transformers)
• Knowledge of .NET integrations with AI services or Go-based backend services for high-performance AI workloads
• Experience developing in secure/controlled environments (FedRAMP, gov-cloud, air-gapped enclaves).
• Understanding of MLOps basics (model versioning, monitoring, CI/CD for AI).
Company:
Quantum Research International offers cyber solutions. Founded in 1987, the company is headquartered in Huntsville, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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