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Vector Databases Jobs in Tennessee (NOW HIRING)

AutoZone

Systems Engineer - Cloud Ops

Memphis, TN · On-site

$54.25 - $72.50/hr

Build and maintain infrastructure for Retrieval-Augmented Generation (RAG) pipelines and vector databases * Configure GPU-enabled node pools and optimize resource allocation for AI/ML workloads

Oracle

Principal Software Engineer

Nashville, TN · On-site

$130K - $174K/yr

Knowledge of LLM orchestration frameworks, retrieval systems, vector databases, or AI infrastructure concepts is a plus. * Demonstrated ability to rapidly ship high quality production systems using ...

AutoZone

Technical Program Manager

Memphis, TN

$125K - $162K/yr

... vector databases, and LLM-based retrieval systems is highly desirable Program Leadership Lead end-to-end execution of search platform initiatives from concept through production Drive alignment ...

Pwc

CTIO AI Engineering Manager

Nashville, TN

$73K - $244K/yr

... vector databases and orchestration tools like LangChain - Translating complex business problems into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...

Apidel Technologies

AI Sr. Engineer LLMOps & MLOps

Memphis, TN · On-site

$93K - $128K/yr

Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. Legacy ...

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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 Tennessee? For Vector Databases jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Tennessee look for? The top searched job categories for Vector Databases jobs in Tennessee are:
What cities in Tennessee are hiring for Vector Databases jobs? Cities in Tennessee with the most Vector Databases job openings:
Vector Databases jobs near you
Systems Engineer - Cloud Ops

Systems Engineer - Cloud Ops

AutoZone

Memphis, TN • On-site

$54.25 - $72.50/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 6 days ago

AutoZone rating

5.3

Company rating: 5.3 out of 10

Based on 1,846 frontline employees who took The Breakroom Quiz

35th of 39 rated national retailers

Job description

Job Description
As a Systems Engineer on the Cloud Operations team, you will be responsible for deploying, managing, and optimizing our cloud-based infrastructure on Google Cloud Platform (GCP). You will work with technologies such as Terraform, Kubernetes (GKE), GitOps/ArgoCD, CI/CD pipelines, and observability tools to ensure reliable, secure, and scalable platform operations.
You will also contribute to our AI/ML platform initiatives, supporting infrastructure for LLM-based applications and AI-powered automation tools that enhance developer productivity and operational efficiency.
You will collaborate with development teams, SREs, and platform architects to ensure seamless deployment and delivery of applications while maintaining the highest standards of reliability, security, and performance.
Responsibilities
Cloud Infrastructure, Automation & Operations:
  • Design, build, and maintain cloud infrastructure using Terraform to automate provisioning, scaling, and lifecycle management of resources on GCP
  • Develop and maintain CI/CD pipelines using GitLab CI to automate build, test, and deployment workflows. Implement and maintain GitOps practices using ArgoCD for declarative, version-controlled application deployment
  • Monitor system performance using observability tools (Dynatrace, Cloud Monitoring, Prometheus/Grafana) and troubleshoot production issues
  • Participate in on-call rotation to provide 24/7 support for critical infrastructure incidents
  • Perform root cause analysis on incidents and implement preventive measures. Document runbooks, architecture decisions, and operational procedures

Kubernetes Platform Management:
  • Deploy, configure, and manage containerized applications on Google Kubernetes Engine (GKE), including GKE Autopilot and Standard clusters
    Manage cluster lifecycle including upgrades, node pool configurations, and capacity planning
  • Troubleshoot pod failures, CrashLoopBackOff, OOMKilled events, and container resource issues
  • Configure and optimize resource requests/limits, Horizontal Pod Autoscaler (HPA), and Vertical Pod Autoscaler (VPA)
  • Manage Kubernetes networking including Services, Ingress controllers, Network Policies, and DNS configurations. Implement and manage service mesh (Istio) for traffic management, observability, and security
  • Manage secrets and configurations using Kubernetes Secrets, ConfigMaps, and external secret management tools. Implement pod security standards, RBAC policies, and workload identity configurations

AI/ML Platform & Automation:
  • Support infrastructure for AI/ML workloads including LLM-based applications and model serving platforms
  • Deploy and manage AI-powered developer tools such as coding assistants (Claude Code, GitHub Copilot) and agentic AI systems. Explore and implement AI-assisted incident response and automated remediation workflows
  • Build and maintain infrastructure for Retrieval-Augmented Generation (RAG) pipelines and vector databases
  • Configure GPU-enabled node pools and optimize resource allocation for AI/ML workloads
  • Implement MCP (Model Context Protocol) servers and AI agent integrations for operational automation
  • Stay current with emerging AI technologies and evaluate their applicability for infrastructure automation

Qualifications
Kubernetes Expertise (Essential):
  • 3+ years hands-on experience with Kubernetes in production environments
  • Deep understanding of Kubernetes architecture: API server, etcd, scheduler, controller manager, kubelet
  • Experience with GKE (Standard and Autopilot modes), including cluster creation, upgrades, and maintenance
  • Proficiency in troubleshooting workloads: analyzing pod logs, events, describe outputs, and container states
  • Strong understanding of resource management: requests, limits, QoS classes, and resource quotas
  • Experience with Kubernetes networking: Services (ClusterIP, NodePort, LoadBalancer), Ingress, Network Policies
  • Knowledge of Kubernetes storage: PersistentVolumes, PersistentVolumeClaims, StorageClasses, dynamic provisioning
  • Experience with Helm charts for application packaging and deployment
  • Familiarity with Kubernetes security: RBAC, Pod Security Standards, Secrets management, Workload Identity
  • Understanding of Kubernetes observability: metrics-server, kubectl top, container resource monitoring
  • Experience debugging common issues: ImagePullBackOff, CrashLoopBackOff, OOMKilled, Evicted pods, pending pods

Cloud & Infrastructure:
  • 3+ years of experience with Google Cloud Platform (GCP) services including GKE, Cloud Run, Cloud SQL, Memorystore, Pub/Sub, and Cloud Logging
  • Strong experience with Terraform for infrastructure as code (IaC)
  • Understanding of cloud networking: VPCs, subnets, firewall rules, Cloud NAT, Private Service Connect

CI/CD & GitOps:
  • Proficiency with GitLab CI/CD pipelines
  • Experience with ArgoCD or similar GitOps tools
  • Understanding of Helm charts and Kustomize for Kubernetes manifest management

Observability & Troubleshooting:
  • Experience with monitoring and APM tools (Dynatrace, Datadog, Prometheus, Grafana)
  • Ability to analyze logs, metrics, and traces to diagnose production issues
  • Familiarity with JVM troubleshooting (heap dumps, thread analysis, GC tuning, connection pool issues)

AI/ML Knowledge:
  • Basic understanding of LLM concepts, prompt engineering, and AI model deployment
  • Familiarity with AI coding assistants and their integration into development workflows
  • Interest in agentic AI systems and autonomous automation tools
  • Exposure to vector databases (Pinecone, Weaviate, pgvector) and RAG architectures is a plus

Systems & Networking:
  • Strong Linux administration skills
  • Understanding of networking concepts (DNS, load balancing, firewalls, TCP/IP)
  • Experience with service mesh (Istio) is a plus

General:
  • Excellent problem-solving and analytical skills
  • Strong written and verbal communication
  • Ability to work effectively in a collaborative, cross-functional environment
  • Experience working in an Agile/DevOps culture
  • Bachelor's degree in Computer Science, Information Technology, or related field (or equivalent experience)

About Us
Since opening our first store in 1979, AutoZone has grown into a leading retailer and distributor of automotive parts and accessories across the Americas. Our customer-first mindset and commitment to Going the Extra Mile define who we are, for both our customers and AutoZoners. Working at AutoZone means being part of a team that values dedication, teamwork, and growth. Whether you're helping customers or building your career, we provide tools and support to help you succeed and drive your future.
Benefits at AutoZone
AutoZone offers thoughtful benefits programs with one-on-one benefits guidance designed to improve AutoZoners' physical, mental and financial well-being.
All AutoZoners (Full-Time and Part-Time):
  • Competitive pay
  • Unrivaled company culture
  • Medical, dental and vision plans
  • Exclusive discounts and perks, including an AutoZone in-store discount
  • 401(k) with company match and Stock Purchase Plan
  • AutoZoners Living Well Program for free mental health support
  • Opportunities for career growth

Additional Benefits for Full-Time AutoZoners:
  • Paid time off
  • Life, and short- and long-term disability insurance options
  • Health Savings and Flexible Spending Accounts with wellness rewards
  • Tuition reimbursement

Minimum age requirements may apply. Eligibility and waiting period requirements may apply; benefits for AutoZoners in Puerto Rico, Hawaii, or the U.S. Virgin Islands may differ. Learn more about all that AutoZone has to offer at Careers.AutoZone.com.
We proudly support Veterans, Active-duty Service Members, Reservists, National Guard and Military Families. Your experience is highly valued, and we encourage you to apply to join our team.
Online Application:
An online application is required. Click the Apply button to complete your application. For step-by-step instructions on how to apply visit careers.autozone.com/candidateresources.
AutoZone, and its subsidiary, ALLDATA are equal opportunity employers. All applicants will be considered for employment without attention to age, race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or any other legally protected categories.

What AutoZone employees say

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Benefits

Hours and flexibility

Workplace

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About AutoZone

Sourced by ZipRecruiter

AutoZone Inc (AutoZone) is a retailer and distributor of automotive replacement parts and accessories. The company provides new and remanufactured automotive hard parts, maintenance items, accessories, and non-automotive products. AutoZone sells automotive diagnostic and repair software through its subsidiary ALLDATA.

Industry

Motor vehicle and motor vehicle parts wholesalers

Company size

10,000+ Employees

Headquarters location

Memphis, TN, US

Year founded

1979

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