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Vector Databases Jobs in Georgetown, TX (NOW HIRING)

Senior AI/ML Software Developer

Austin, TX

$54 - $71.25/hr

Proficiency in SQL (PostgreSQL, MySQL) and NoSQL/vector databases. • Scripting: Proficient in both Bash and PowerShell for automation workflows. Preferred Qualifications • Experience with ...

New

Gen AI / Agentic AI Lead

Austin, TX · On-site

$138K - $170K/yr

... vector databases (FAISS, Pinecone, Weaviate, Azure AI Search). • Familiarity with cloud platforms and Gen AI services (AWS, Azure, GCP). • Experience with REST API development (FastAPI, Flask ...

... and vector databases Knowledge of current Gen AI research and techniques in one or more of the following areas: RAG systems, Agentic AI (multi-agent orchestration, tool calling), or Prompt ...

... and vector databases Knowledge of current Gen AI research and techniques in one or more of the following areas: RAG systems, Agentic AI (multi-agent orchestration, tool calling), or Prompt ...

Familiarity with vector databases, embeddings, and retrieval systems * Experience building internal tooling or customer-facing features powered by AI * Knowledge of model evaluation, observability ...

Gen AI Software Engineer

Austin, TX · On-site

$147K - $272K/yr

... and vector databases Knowledge of current Gen AI research and techniques in one or more of the following areas: RAG systems, Agentic AI (multi-agent orchestration, tool calling), or Prompt ...

... and vector databases Knowledge of current Gen AI research and techniques in one or more of the following areas: RAG systems, Agentic AI (multi-agent orchestration, tool calling), or Prompt ...

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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 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 are popular job titles related to Vector Databases jobs in Georgetown, TX? For Vector Databases jobs in Georgetown, TX, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Georgetown, TX look for? The top searched job categories for Vector Databases jobs in Georgetown, TX are:
What cities near Georgetown, TX are hiring for Vector Databases jobs? Cities near Georgetown, TX with the most Vector Databases job openings:

Machine Learning Engineer - TS with Security Clearance

webAI, Inc

Austin, TX

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 12 days ago


Job description

*This position is NOT contingent upon awarding of a project or needing a funding source. This is full-time employment with webAI.* About the Role: We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments. You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments. Responsibilities: -Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems.
-Productionize AI models from research prototypes into scalable, deployable systems used in real world applications.
-Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization.
-Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration.
-Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval.
-Work with multi-modal AI systems across computer vision, audio, and natural language domains.
-Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions. Qualifications: -Active US Security clearance or eligibility and willingness to obtain a US Security clearance
-5+ years of experience in applied AI, ML engineering, or production AI systems.
-Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers.
-Proven experience deploying AI models across cloud, edge, and mobile hardware environments.
-Expertise in model compression and optimization (quantization, pruning, distillation).
-Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone).
-Familiarity with multi-modal models and synthetic data generation methods.
-Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments. Preferred Skills: -Experience with edge AI, federated learning, or offline inference systems.
-Understanding of AI governance and compliance frameworks relevant to public sector deployments.
-Experience integrating models into large scale distributed systems or microservice architectures.
-Excellent communication and technical documentation skills for collaboration across multi disciplinary teams.
-Strong understanding of GPU computing, CUDA, and performance profiling. We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following: Truth - Emphasizing transparency and honesty in every interaction and decision. Ownership - Taking full responsibility for one’s actions and decisions, demonstrating commitment to the success of our clients. Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement. Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others. Benefits: -Competitive salary and performance-based incentives.
-Comprehensive health, dental, and vision benefits package.
-401k Match (US-based only)
-$200/mos Health and Wellness Stipend
-$400/year Continuing Education Credit
-$500/year Function Health subscription (US-based only)
-Free parking, for in-office employees
-Unlimited Approved PTO
-Parental Leave for Eligible Employees
-Supplemental Life Insurance webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.