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Senior Fastapi Developer Jobs in San Diego, CA (NOW HIRING)

iQuasar LLC

Cloud Application Engineer

San Diego, CA · On-site

$135K - $145K/yr

Design and develop APIs and backend services using Python, FastAPI, GraphQL, REST. * Build data ... Regards, Zain Wani Sr. Recruitment Professional iQuasar LLC Sterling, VA, 20165 Direct: (703) 349 ...

IQUASAR LLC

Cloud Application Engineer

San Diego, CA · On-site

Design and develop APIs and backend services using Python, FastAPI, GraphQL, REST. * Build data ... Regards, Zain Wani Sr. Recruitment Professional iQuasar LLC Sterling, VA, 20165 Direct: (703) 349 ...

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Senior Fastapi Developer information

See San Diego, CA salary details

$16

$65

$93

How much do senior fastapi developer jobs pay per hour?

As of Sep 1, 2026, the average hourly pay for senior fastapi developer in San Diego, CA is $65.54, according to ZipRecruiter salary data. Most workers in this role earn between $55.62 and $73.51 per hour, depending on experience, location, and employer.

What does a Senior FastAPI Developer do?

A Senior FastAPI Developer designs, develops, and maintains backend web applications using the FastAPI framework in Python. They are responsible for building scalable APIs, optimizing performance, ensuring security best practices, and integrating with databases and external services. Senior developers also mentor junior team members, contribute to architecture decisions, and collaborate closely with front-end engineers and stakeholders to deliver robust software solutions.

What are the key skills and qualifications needed to thrive as a Senior FastAPI Developer?

To thrive as a Senior FastAPI Developer, you need deep expertise in Python, RESTful API design, and experience building scalable web backends, typically supported by a degree in computer science or related field. Familiarity with FastAPI, Docker, cloud platforms (such as AWS or Azure), and CI/CD tools is highly valued, along with proficiency in testing frameworks. Strong problem-solving, leadership, and communication skills help you collaborate effectively with cross-functional teams and mentor junior developers. These skills enable the delivery of robust, efficient, and maintainable APIs that support business goals and high user demands.

What are some common challenges faced by Senior FastAPI Developers when scaling applications, and how can these be addressed?

Senior FastAPI Developers often encounter challenges related to managing high concurrency, optimizing API performance, and ensuring robust security as applications scale. To address these, it’s important to implement asynchronous programming practices, leverage efficient database queries, and utilize API gateways for better traffic management. Collaboration with DevOps and QA teams is also essential to automate deployments and monitor system health, ensuring smooth scaling and reliable user experiences.

What are the most commonly searched types of Fastapi Developer jobs in San Diego, CA?

The most popular types of Fastapi Developer jobs in San Diego, CA are:

What are popular job titles related to Senior Fastapi Developer jobs in San Diego, CA?

For Senior Fastapi Developer jobs in San Diego, CA, the most frequently searched job titles are:

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What cities near San Diego, CA are hiring for Senior Fastapi Developer jobs?

Cities near San Diego, CA with the most Senior Fastapi Developer job openings:

Senior Fastapi Developer jobs near you
Infographic showing various Senior Fastapi Developer job openings in San Diego, CA as of August 2026, with employment types broken down into 77% Full Time, 9% Part Time, and 14% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $136,324 per year, or $65.5 per hour.

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

DivIHN

San Diego, CA • On-site, Remote

$110K - $152K/yr

Contractor

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

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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