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Generative Ai Project Manager Jobs in Riverside, CA

CYNET SYSTEMS

AI Scientist / AI Architect

Irvine, CA · On-site

$75 - $80/hr

Hands-on experience with LLM-enabled applications and Generative AI. * Experience building agent ... Experience with CI/CD pipelines and environment management. * Strong understanding of observability ...

Turion Space

Lead AI Engineer

Irvine, CA

$110K - $144K/yr

Proven ability to lead and mentor technical teams, with strong project management skills ... Expertise in advanced AI techniques, including reinforcement learning, generative models, and large ...

FieldAI

Project Manager

Irvine, CA · On-site

$130K - $200K/yr

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the ...

FieldAI

Project Manager

Irvine, CA · On-site

$130K - $200K/yr

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the ...

FieldAI

Project Manager

Irvine, CA · On-site

$130K - $200K/yr

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the ...

FieldAI

Project Manager

Irvine, CA · On-site

$130K - $200K/yr

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the ...

FieldAI

Project Manager

Irvine, CA

$130K - $200K/yr

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the ...

FieldAI

Project Manager

Irvine, CA

$130K - $200K/yr

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the ...

Newegg

Business System Developer

Ontario, CA · On-site

* Develop, maintain, and enhance e-commerce logistics systems to support inbound receiving, outbound shipping, inventory management, and returns processing. * Implement Generative AI-powered solutions ...

Yum! Brands

Sr. Product Manager I

Irvine, CA · Hybrid

$135K - $179K/yr

Experience with tools such as JIRA, Confluence, Microsoft Office and generative AI Work-Hard, Play ... project management skills with the ability to manage multiple complex initiatives. Excellent ...

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Showing results 41-60

All JobsGenerative Ai Project JobsGenerative Ai Project Manager JobsGenerative Ai Project Manager Jobs in Riverside, CA

Generative Ai Project Manager information

See Riverside, CA salary details

$40.2K

$107.1K

$169K

How much do generative ai project manager jobs pay per year?

As of Aug 7, 2026, the average yearly pay for generative ai project manager in Riverside, CA is $107,125.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,900.00 and $128,300.00 per year, depending on experience, location, and employer.

What are some unique challenges faced by generative AI project managers when overseeing cross-functional teams?

Generative AI Project Managers often encounter the challenge of bridging knowledge gaps between technical AI specialists, such as data scientists and engineers, and non-technical stakeholders, like product managers or business leaders. Coordinating clear communication and aligning project goals requires balancing rapid technological changes with business requirements, all while ensuring ethical and responsible AI development. Additionally, managing timelines can be complex due to the experimental nature of generative AI projects, which may involve iterative prototyping and unexpected roadblocks. Building trust and facilitating collaboration across diverse teams is key to project success.

What is the difference between Generative Ai Project Manager vs Data Scientist?

AspectGenerative Ai Project ManagerData Scientist
Required CredentialsProject management certifications, AI knowledgeDegree in Data Science, Computer Science, or related fields
Work EnvironmentCross-functional teams, project planningData analysis, model development
Employer & Industry UsageTech companies, AI startups, R&D departmentsTech firms, research institutions, analytics companies

While both roles involve AI, the Generative Ai Project Manager oversees AI projects, coordinating teams and timelines, whereas the Data Scientist focuses on analyzing data and building models. The project manager ensures project delivery, while the data scientist develops the AI models used within projects.

What does a generative AI project manager do?

A Generative AI Project Manager oversees projects that involve the development and implementation of generative artificial intelligence solutions. Their responsibilities include coordinating teams of data scientists, engineers, and designers, managing project timelines and budgets, and ensuring that deliverables meet business objectives. They also facilitate communication between technical and non-technical stakeholders to ensure alignment and project success. Additionally, they stay updated on advances in AI technology to guide project direction and innovation.

What are the key skills and qualifications needed to thrive as a generative AI project manager?

To thrive as a Generative AI Project Manager, you need a solid understanding of AI/machine learning concepts, project management methodologies, and a relevant degree (such as computer science or engineering). Familiarity with tools like Jira, Agile frameworks, and AI platforms (e.g., TensorFlow, PyTorch) as well as certifications like PMP or Agile Scrum Master are highly beneficial. Strong leadership, communication, and problem-solving skills set outstanding candidates apart by enabling them to bridge technical and non-technical teams. These abilities are crucial for delivering AI projects on time, ensuring alignment with business goals, and adapting to rapidly evolving technology landscapes.
What are popular job titles related to Generative Ai Project Manager jobs in Riverside, CA? For Generative Ai Project Manager jobs in Riverside, CA, the most frequently searched job titles are:
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What cities near Riverside, CA are hiring for Generative Ai Project Manager jobs? Cities near Riverside, CA with the most Generative Ai Project Manager job openings:
Generative Ai Project Manager jobs near you
Infographic showing various Generative Ai Project Manager job openings in Riverside, CA as of August 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution, with an average salary of $107,125 per year, or $51.5 per hour.

Senior AI Engineer (GenAI and Data Platform - AWS)

Saransh Inc

Irvine, CA • On-site

$131K - $173K/yr

Contractor

Re-posted 20 days ago

Job description

Role: Senior AI Engineer (GenAI + Data Platform – AWS)
Location: 4 days a week onsite is must (3 days in Irvine, CA & 1 Day in Downtown, LA, CA)
Job Type: Contract

Role Summary:
  • We are seeking a Senior AI Engineer to design, build, and scale a production-grade Generative AI and Data Platform on AWS.
  • The role focuses on enabling LLM-powered capabilities through vector search, graph-based knowledge systems, and governed data pipelines.
Note:
Must Have Skills:
  • Generative AI / LLM (RAG, embeddings, prompt engineering)
  • AWS Cloud (OpenSearch, Neptune, DynamoDB, ElastiCache/Redis)
  • Vector Search & Retrieval Systems (OpenSearch / vector DB)
  • Graph Databases (Amazon Neptune, knowledge graphs)
  • LLM Frameworks (LangChain / LlamaIndex)
  • Agentic AI Frameworks (LangGraph / AutoGen / CrewAI)
  • Databricks & Apache Spark (data pipelines, embedding pipelines)
  • Backend/API Development (Python, scalable APIs, microservices)

Must Have Certifications:
AWS Certification (Preferred):
  • AWS Certified Solutions Architect OR
  • AWS Certified Machine Learning Specialty OR
  • AWS Data Engineer Certification

The ideal candidate will own end-to-end delivery across the AI lifecycle, including:
  • Data ingestion and knowledge curation
  • Embeddings and retrieval systems
  • Backend services and APIs
  • CI/CD pipelines and deployment
Key Responsibilities:
1. GenAI Enablement & Integration
 
Build and operationalize LLM-powered applications using:
  • Retrieval-Augmented Generation (RAG)
  • Embeddings pipelines
  • Prompt orchestration and evaluation frameworks
  • Design and implement vector search systems using Amazon OpenSearch
  • Develop graph-based knowledge systems using Amazon Neptune for relationships, lineage, and explainability
Integrate supporting infrastructure:
  • Amazon ElastiCache (Redis) for session state and caching
  • DynamoDB for scalable, low-latency data access
 
Implement agentic workflows using frameworks such as:
LangGraph, AutoGen, CrewAI (or equivalent)
Integrate with LLM frameworks like:
LangChain, LlamaIndex (tool calling, retrieval orchestration, context management)
Define standards for:
Tool integration
Context-sharing patterns (MCP-style designs)
Evaluate LLM models and retrieval strategies across:
Latency
Cost
Accuracy
Context limitations
 
2. Data Pipelines & Knowledge Engineering
Design and build scalable data pipelines using Databricks and Apache Spark
Implement:
  • Data ingestion and transformation pipelines
  • Document processing (chunking, metadata tagging)
  • Embedding generation and indexing
Ensure high data quality standards:
Validation, completeness, consistency, monitoring
 
Implement data governance frameworks:
  • Data classification and access controls
  • Retention policies
  • Auditability and lineage tracking
3. Backend Services & APIs
Develop backend services exposing AI capabilities through secure and scalable APIs
Define best practices for:
API contracts and versioning
Reliability (retry logic, circuit breakers, idempotency)
Enable reusability of platform capabilities across teams and applications.
 
4. Deployment, MLOps & Operational Excellence
Build and manage CI/CD pipelines for AI and data workloads
Deploy production systems using:
Docker (containerization)
Kubernetes (orchestration)
Implement deployment strategies:
Blue/green deployments
Canary releases
Rollback strategies
Feature flags
Ensure system reliability through:
Monitoring (latency, failures, cost, data freshness)
Alerting and observability
Secrets management and least-privilege access
Optimize platform performance and cost
 
5. LLM Observability, Evaluation & Quality
Define and track GenAI quality metrics:
Grounding / faithfulness
Retrieval relevance
Response consistency
Latency and cost per request
Implement:
Prompt/version tracking
Offline evaluation pipelines
Continuous improvement workflows
 
6. LLM Security, Safety & Compliance
Implement secure AI systems with:
Access control and authentication
Data protection policies
Responsible AI guardrails
Ensure compliance with best practices in:
AI safety
Data privacy
Monitoring and auditability
Required Skills:
  • Strong experience in Generative AI / LLM systems (RAG, embeddings, prompt engineering)
  • Hands-on experience with AWS ecosystem
Expertise in:
  • OpenSearch (vector search)
  • Neptune (graph databases)
  • DynamoDB and Redis (ElastiCache)
Experience with:
  • LangChain / LlamaIndex
  • Agentic AI frameworks (LangGraph, AutoGen, CrewAI)
  • Strong programming skills (Python preferred)
  • Experience with Databricks and Apache Spark
Solid understanding of:
  • Data pipelines
  • Distributed systems
  • API design
Preferred Skills:
Experience with:
  • Model evaluation frameworks and LLM observability tools
  • AI governance and compliance frameworks
  • Kubernetes and advanced MLOps practices
Familiarity with:
  • Model Context Protocol (MCP) patterns
  • Agent-based architectures
Qualifications:
  • Bachelor’s or Master’s degree in: Computer Science / Data Science / AI / related field
  • Proven experience building production-grade AI platforms and systems
  • Strong background in end-to-end AI/ML lifecycle delivery.