1

Qdrant Jobs in California (NOW HIRING)

Has exposure to AI application frameworks such as LangChain or LlamaIndex, or has basic experience with vector databases (e.g., Qdrant, pgvector). * Understands the basics of MCP (Model Context ...

Has exposure to AI application frameworks such as LangChain or LlamaIndex, or has basic experience with vector databases (e.g., Qdrant, pgvector). * Understands the basics of MCP (Model Context ...

AI Native Transformation Manager

Culver City, CA · On-site

$124.30K - $127K/yr

Hands-on experience with vector databases and RAG architectures (Qdrant, Pinecone, ChromaDB, Weaviate) * Understanding of graph databases and knowledge graphs (Neo4j, Neptune) for semantic ...

Senior Staff Machine Learning Engineer

Palo Alto, CA

$122.80K - $168.70K/yr

... Qdrant), data warehouse (e.g. snowflake), streaming platform (e.g. Kafka), relational database (e.g. postgresql), Nosql (e.g. MongoDB, Cassandra), distributed processing (e.g. Spark, Ray), workflow ...

Has exposure to AI application frameworks such as LangChain or LlamaIndex, or has basic experience with vector databases (e.g., Qdrant, pgvector). * Understands the basics of MCP (Model Context ...

Staff Machine Learning Engineer

Palo Alto, CA · On-site +1

$130K - $260K/yr

... Qdrant), data warehouse (e.g. snowflake), streaming platform (e.g. Kafka), relational database (e.g. postgresql), Nosql (e.g. MongoDB, Cassandra), distributed processing (e.g. Spark, Ray), workflow ...

Senior Data Scientist

Marina Del Rey, CA · On-site

$200K - $225K/yr

Snowflake, Qdrant * Data Processing: Ray, Pandas, DBT, FastAPI, Airflow, Astronomer, DBOS * DevOps: Github Actions, Docker, Terraform, Kubernetes, ArgoCD, AWS, GCP, Datadog * MLOps & Inference:

next page

Showing results 1-20

Qdrant information

How does Qdrant compare to Pinecone?

Qdrant and Pinecone are both vector similarity search platforms used in AI and machine learning applications. Qdrant is open-source and offers flexible deployment options, while Pinecone is a managed service with a focus on scalability and ease of use. Both require knowledge of vector search concepts and can be integrated with various data processing tools.

What is the difference between Qdrant vs Data Scientist?

AspectQdrantData Scientist
Required CredentialsTechnical certifications, knowledge of vector databasesDegree in Data Science, Statistics, or related field
Work EnvironmentTech companies, startups, AI-focused firmsResearch labs, tech companies, consulting firms
Industry UsageAI, machine learning, data storageData analysis, predictive modeling, research

Qdrant primarily focuses on managing and deploying vector similarity search databases, requiring technical skills in database management and AI tools. Data Scientists analyze data, build models, and interpret results. While both roles operate within the tech and AI industry, Qdrant specialists are more technical and infrastructure-oriented, whereas Data Scientists focus on data analysis and modeling.

What cities in California are hiring for Qdrant jobs? Cities in California with the most Qdrant job openings:
Infographic showing various Qdrant job openings in California as of May 2026, with employment types broken down into 96% Full Time, and 4% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.
AI Solutions Architect Agentic AI Platforms

AI Solutions Architect Agentic AI Platforms

BayOne Solutions

San Jose, CA • On-site

$80 - $90/hr

Contractor

This job post has expired today. Applications are no longer accepted.


Job description

Position: AI Solutions Architect — Agentic AI Platforms

Location: San Francisco, CA (Onsite)

Duration: 12+ Months Contract

Pay Rate: $80-90/hr on W2/C2C

AI Solutions Architect — Agentic AI Platforms

ROLE OVERVIEW

BayOne is hiring an AI Solutions Architect to lead the design and delivery of our enterprise Agentic AI

platform. You will own the technical vision for multi-agent systems, RAG, MCP-based tool integration,

and the underlying microservices that let enterprises compose, govern, and operate domain-specific

agents at scale — and drive reusability by codifying patterns into shared skills and sub-agents across

the Application Development Lifecycle (ADLC).

KEY RESPONSIBILITIES

• Agentic AI Architecture: Own end-to-end design of multi-agent systems using LangChain,

LangGraph, and Model Context Protocol (MCP) — including planner-executor patterns, sub-agent

hierarchies, tool routing, retries, and cost-aware token budgeting.

• RAG & Knowledge Systems: Architect production-grade RAG pipelines with vector databases

(pgvector, Qdrant), hybrid retrieval, re-ranking, and document-aware chunking to ground agents in

enterprise knowledge.

• Solution Architecture: Design reference architectures and solution blueprints for enterprise

clients across BFSI, payments, manufacturing, and government — translating business outcomes

into agentic AI roadmaps and reusable accelerators.

• Scalable Microservices: Build event-driven microservices on Kafka, polyglot data layers with

PostgreSQL and vector DBs, and Kubernetes-based deployment topologies for high-throughput

inference workloads.

• MLOps & Model Lifecycle: Establish practices spanning training, fine-tuning, prompt and config

versioning, structured evaluations against golden datasets, drift detection, and automated rollback

when output quality degrades.

• Traceability & Observability: Instrument agent reasoning traces, tool-call audit trails, token

spend, and quality signals with Prometheus, Grafana, and OpenTelemetry — enabling policy

enforcement and human-in-the-loop oversight.

• Reusable Engineering Standards: Codify AI engineering patterns (RAG retrievers, agent loops,

eval harnesses, traceability spans) into reusable skills, sub-agents, and platform components

consumed across multiple product lines.

• Rapid Engineering in ADLC: Roll out AI-led developer tools and sub-agents (Claude Code,

Playwright MCP) across planning, code generation, code review, test authoring, and release

validation — accelerating delivery while standardizing quality.

• Presales & Client Engagement: Partner with sales, presales, and customer success on

enterprise pursuits — authoring solution designs, leading technical workshops, and shaping agentic

AI roadmaps for prospects and existing clients.

REQUIRED QUALIFICATIONS

• 10+ years of software engineering experience, with at least 3 years architecting LLM-based or

agentic AI systems in production.

• Deep hands-on expertise with LangChain, LangGraph, RAG, MCP, prompt engineering, context

engineering, and token optimization.

• Strong programming skills in Python and TypeScript (Java a plus); proven ability to design and

implement microservices with FastAPI, Spring Boot, or Node.js.

• Production experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, Terraform,

and CI/CD pipelines.

• Solid grounding in MLOps — model training/fine-tuning, evaluation pipelines, drift detection, and

observability for AI systems.

• Track record of solution architecture for enterprise clients — translating business problems into

reference architectures and shipping production outcomes.

• Bachelor's degree in Computer Science, Engineering, or a related field.

NICE TO HAVE

• Experience in Retail or regulated industries (PCI-DSS, KYC/AML, audit & compliance

workflows).

• Familiarity with API gateways (Apigee, Kong) and developer-portal design for partner onboarding.

• Exposure to Claude Code, Cursor, or similar agentic coding environments and the design of reusable skills/sub-agents

BayOne is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any federal, state, or local protected class. This job posting represents the general duties and requirements necessary to perform this position and is not an exhaustive statement of all responsibilities, duties, and skills required. Management reserves the right to revise or alter this job description.


BayOne Solutions logo

About BayOne Solutions

Sourced by ZipRecruiter

BayOne is a minority owned Talent Solutions Partner based in the Bay Area, and we have a passion for diversity in the Tech Industry. We help companies build teams. We specialize in the following domains: Project & Program Management, Cloud Computing & IT Infrastructure Management, Big Data Services, Software & Quality Engineering, User Experience Design. We help companies to solve their talent gap by providing qualified experts on demand, training their legacy work force on future technologies, and automating their business processes.

Industry

It services

Company size

201 - 500 Employees

Headquarters location

Pleasanton, CA, US

Year founded

2012