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Senior Generative Ai Prompt Engineer Jobs in California

Esri

Sr. Generative AI Software Developer

Redlands, CA · On-site

$54.75 - $72.50/hr

Esri's Professional Services division is seeking an experienced Sr. Software Development Engineer ... test, and integrate AI/ML algorithms as part of product releases and evolve data quality ...

Avathon

Senior AI Engineer

Pleasanton, CA · On-site

$116K - $159K/yr

Responsibilities : • Design, develop, and deploy machine learning and Generative AI solutions to ... prompt engineering, model alignment, evaluation, and performance benchmarking for GenAI ...

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Senior Generative Ai Prompt Engineer information

What is a senior generative AI prompt engineer?

A Senior Generative AI Prompt Engineer is an experienced professional who specializes in designing, optimizing, and managing prompts for AI models, such as large language models (LLMs) like GPT-4. Their role involves understanding how AI responds to different instructions, creating effective prompts to achieve desired outputs, and collaborating with teams to integrate AI-generated content into products or workflows. They often work closely with data scientists, software engineers, and product managers to ensure the AI delivers accurate and valuable results. Additionally, they may be responsible for training others on prompt engineering best practices and staying updated on the latest advancements in generative AI.

What are the key skills and qualifications needed to thrive as a senior generative AI prompt engineer?

To thrive as a Senior Generative AI Prompt Engineer, you need expertise in natural language processing, machine learning, and prompt engineering, typically supported by an advanced degree in computer science or a related field. Proficiency with AI frameworks like TensorFlow or PyTorch, experience with large language models (e.g., GPT, Llama), and familiarity with cloud platforms are essential. Strong problem-solving abilities, creativity, and effective communication skills help in designing, iterating, and collaborating on high-quality AI-driven solutions. These skills ensure the development of robust, innovative models and effective deployment of generative AI solutions to meet business objectives.

What are some common challenges faced by senior generative AI prompt engineers, and how can they be addressed?

Senior Generative AI Prompt Engineers often encounter challenges such as designing prompts that consistently yield high-quality outputs and managing model biases. Collaborating closely with data scientists, product managers, and other engineers is essential to iterate on prompt strategies and evaluate results. Staying updated with rapid advancements in generative AI models and understanding the nuances of prompt tuning are also key aspects of the role. Regularly reviewing model outputs and leveraging feedback from cross-functional teams can help address these challenges and drive continuous improvement.

What is the difference between Senior Generative Ai Prompt Engineer vs Machine Learning Engineer?

AspectSenior Generative Ai Prompt EngineerMachine Learning Engineer
CredentialsBachelor's or higher in CS, AI, or related fields; experience with NLP and prompt designBachelor's or higher in CS, Data Science, or related fields; expertise in algorithms and model development
Work EnvironmentFocus on prompt crafting, AI interaction, and fine-tuning generative modelsDeveloping, training, and deploying machine learning models across various applications
Industry UsagePrimarily in AI product teams, content generation, and conversational AIAcross tech, finance, healthcare, and more for predictive analytics and automation

While both roles involve AI and machine learning, the Senior Generative Ai Prompt Engineer specializes in designing prompts for generative models, whereas the Machine Learning Engineer develops and optimizes machine learning algorithms for broader applications.

What are the most commonly searched types of Generative Ai Prompt Engineer jobs in California?

The most popular types of Generative Ai Prompt Engineer jobs in California are:

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For Senior Generative Ai Prompt Engineer jobs in California, the most frequently searched job titles are:

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Senior Generative Ai Prompt Engineer jobs near you
Infographic showing various Senior Generative Ai Prompt Engineer job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote

DivIHN

San Diego, CA • On-site, Remote

$110K - $152K/yr

Contractor

Posted 3 days ago

New

Job description

For further inquiries about this opportunity, please contact one of our Talent Specialists, Justeen at (224)-394-4903
Title: Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote
Duration: 6 Months with possible for extension
Location: Remote
Candidates must be available to work West Coast (PST) hours.
Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered.
Day to Day Responsibilities
  • RAG & Prompt Engineering: Craft and refine effective prompts for RAG, grounding, and context tuning to achieve optimal AI performance in product development.
  • High-Performance API Engineering: Develop asynchronous microservices (FastAPI) using Server-Sent Events (SSE) or WebSockets to stream real-time LLM responses to front-end UIs without backend timeouts.
  • Vector Database Infrastructure: Design, develop, and implement robust Vector Databases using LLMs and modern retrieval technologies to capture information from diverse engineering sources (PDFs, design docs, regulatory guidelines).
  • Data Extraction & Structuring Pipelines: Build and optimize pipelines to extract and structure multi-modal data (tables, text, images) from unstructured documents for LLM training, grounding, and runtime query execution.
  • LLM Fine-Tuning & Training: Fine-tune and train generative AI models using client's engineering data and domain knowledge to create high-accuracy, domain-specific models.
  • GenAI Application & Tool Development: Design and implement scalable backend APIs (FastAPI/REST) and UI integration interfaces so internal engineers can query knowledge bases and analyze data.
  • Automated Requirements Generation: Develop backend functionalities to automatically generate technical requirements from design documents, user stories, and system specification files.
  • Documentation & Knowledge Transfer: Thoroughly document architecture, code, REST endpoints, and model training procedures to enable seamless knowledge transfer to client's internal teams.
  • Cross-Functional Collaboration: Partner closely with Subject Matter Experts (SMEs), System Engineers, and V&V Test teams to optimize AI-powered workflows.
  • AI Guardrails, MLOps & Cost Governance: Implement hallucination checks, PII masking, and guardrails (e.g., NeMo Guardrails) for medical device context. Track token usage, latency, and costs using LangSmith or Vertex AI monitoring.
  • Collaborate closely with engineers and cross-functional teams to design and develop AI solutions.
  • Develop and integrate Generative AI applications and APIs.
  • Work with large datasets, document processing, and data pipelines.
  • Participate in problem-solving, solution design, and technical discussions.
  • Communicate effectively with team members and stakeholders.

Must-Have Qualifications :
  • Relevant Experience & STEM Foundation: 4+ years of professional software/ML engineering experience, with a dedicated AI/ML focus in the last 1-2 years.
  • Google Gemini / Vertex AI (Non-negotiable): Hands-on experience with the Gemini model family and Vertex AI, including deployment, grounding, and integration into production AI services. Hands-on experience with containerization (Docker) and deploying services via Cloud Run or GKE (Kubernetes).
  • Languages & AI Libraries: Proficiency in Python and modern ML/AI frameworks (PyTorch, LangChain, LangSmith) for building autonomous LLM agents, tools, and RAG pipelines.
  • Agent Building & Tool Calling: Proven experience building AI/LLM agents and tool-calling systems in Python against unstructured, multi-source data.
  • Context Engineering & RAG: Expertise in RAG pipelines, prompt engineering, context tuning, grounding, and Vector Databases (e.g., Milvus, Postgres/Pgvector). Clear understanding of advanced RAG architecture including Hybrid Search (Vector + Keyword), Re-ranking models, and semantic caching.
  • Unstructured Data Handling (Non-negotiable): Demonstrated ability to ingest, clean, extract, and structure text, tables, and images from unstructured documents (PDFs, design docs, regulatory files) for LLM training and usage.

Role Information
  • Generative AI Engineer role.
  • Experience in API development.
  • Hands-on experience with Google Gemini and Vertex AI.
  • Knowledge of Agentic AI frameworks and architectures.
  • Strong Python development skills, including experience with LangChain and related AI frameworks.
  • Open to candidates from any industry domain.

Required Skills :
  1. Full Stack Engineering - Building applications for AI-powered services (Backend APIs + Front-End Integration).
  2. Generative AI & LLM Platforms - 2+ years building RAG pipelines & LLM apps using Python, LangChain, and LangSmith.
  3. Agent Building & Unstructured Data - Building AI agents/tool-calling systems in Python and handling unstructured, multi-source data extraction (PDFs, docs).
  4. Strong hands-on experience in Artificial Intelligence and Generative AI
  5. Python programming expertise.
  6. Experience with LangChain and Agentic AI frameworks.
  7. Understanding data handling to pull data from PDFs, design docs, regulatory files

Preferred Skills :
  1. Direct experience architecting and serving custom REST APIs.
  2. Experience in regulated / compliance-sensitive domains (e.g., healthcare / medical device guidelines like FDA, ISO 13485).
  3. Experience integrating multiple LLM APIs beyond a single provider (OpenAI, Bedrock, Claude, or similar).

Additional Preferred Skills
  • Direct experience designing and deploying high-throughput REST APIs (e.g., FastAPI/Flask).
  • Familiarity with medical device development regulations and compliance (e.g., FDA guidelines, ISO 13485).
  • Experience integrating multiple LLM APIs beyond a single vendor (e.g., OpenAI, AWS Bedrock, Anthropic Claude).
  • Front-end development/integration experience for UI design (e.g., Streamlit, Gradio, React/Next.js integration).

Education Requirements:
  • Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years

Required Testing:
  • Technical evaluation of Python proficiency, RAG architecture concepts, and API/Agent design.

Software Skills Required:
  • Languages: Python (AsyncIO, OOP), SQL.
  • AI & Agent Frameworks: PyTorch, LangChain, LangSmith, Vertex AI SDK.
  • API & Web Frameworks: FastAPI, Flask, REST APIs, Server-Sent Events (SSE).
  • Databases & Search: Milvus, Pgvector, Qdrant, Redis (caching).
  • Front-End Integration: Streamlit, Gradio, basic React/Next.js.
  • Testing & Guardrails: pytest, JUnit, LangSmith evaluation, NeMo Guardrails.
  • DevOps & Cloud: Docker, GCP (Vertex AI, Cloud Run, GKE), Git.

Required Certifications:
  • Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus.

Interview
  • Number of Interviews: 1,
  • Web Conference (Zoom/ TEAMs)
  • Live problem-solving and technical exercise during the interview.

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