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Associate Fastapi Jobs in Santa Clara, CA (NOW HIRING)

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Associate Fastapi information

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$12

$23

$38

How much do associate fastapi jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for associate fastapi in Santa Clara, CA is $23.33, according to ZipRecruiter salary data. Most workers in this role earn between $17.50 and $24.28 per hour, depending on experience, location, and employer.

What is an associate FastAPI developer?

An Associate FastAPI developer is an entry- to mid-level software professional who specializes in building web applications and APIs using FastAPI, a modern Python web framework. Their responsibilities often include developing endpoints, integrating databases, writing unit tests, and maintaining code quality. They typically work as part of a larger development team, assisting in the design and implementation of scalable and high-performance web services. Associate FastAPI developers are expected to have a good understanding of Python, RESTful API principles, and basic knowledge of asynchronous programming.

What are the key skills and qualifications needed to thrive as an associate FastAPI developer?

To thrive as an Associate FastAPI Developer, you need a strong understanding of Python programming, RESTful API design, and experience with the FastAPI framework, often supported by a relevant degree or coding bootcamp. Familiarity with databases (like PostgreSQL or MongoDB), version control systems (such as Git), and containerization tools (like Docker) is typically expected. Excellent problem-solving abilities, attention to detail, and effective communication skills help you collaborate with teams and deliver robust solutions. These skills ensure efficient, scalable API development and seamless integration within modern software engineering environments.

What are common challenges faced by an associate FastAPI developer, and how can I prepare for them?

As an Associate FastAPI Developer, you may encounter challenges such as understanding asynchronous programming patterns, integrating FastAPI with various databases, and ensuring robust API documentation. It's also common to collaborate closely with frontend teams and DevOps engineers, which requires clear communication and an understanding of deployment workflows. To prepare, familiarize yourself with Python async features, explore automated API documentation tools like Swagger, and practice deploying FastAPI applications in cloud or containerized environments.

What is the difference between Associate Fastapi vs Associate Python Developer?

AspectAssociate FastapiAssociate Python Developer
Required CredentialsKnowledge of Fastapi, Python, REST APIsProficiency in Python, basic web frameworks
Work EnvironmentBackend development, API design, cloud deploymentApplication development, scripting, software projects
Employer & Industry UsageTech companies, startups, SaaS providersSoftware firms, tech departments, startups
Common Search & ComparisonYesYes

The main difference between an Associate Fastapi and an Associate Python Developer lies in their focus areas. An Associate Fastapi specializes in building APIs using the Fastapi framework, requiring specific knowledge of Fastapi and REST API design. In contrast, an Associate Python Developer has broader Python programming skills applicable to various software development tasks. Both roles are common in tech environments, but Fastapi roles are more specialized towards API development and deployment.

What are the most commonly searched types of Fastapi jobs in Santa Clara, CA?

The most popular types of Fastapi jobs in Santa Clara, CA are:

What are popular job titles related to Associate Fastapi jobs in Santa Clara, CA?

For Associate Fastapi jobs in Santa Clara, CA, the most frequently searched job titles are:

What job categories do people searching Associate Fastapi jobs in Santa Clara, CA look for?

The top searched job categories for Associate Fastapi jobs in Santa Clara, CA are:

What cities near Santa Clara, CA are hiring for Associate Fastapi jobs?

Cities near Santa Clara, CA with the most Associate Fastapi job openings:

Associate Fastapi jobs near you
Infographic showing various Associate Fastapi job openings in Santa Clara, CA as of June 2026, with employment types broken down into 1% As Needed, 64% Full Time, 33% Part Time, 1% Temporary, and 1% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $48,536 per year, or $23.3 per hour.

AI Application Engineer / Lead

Santa Clara, CA • On-site

Mind Ware Inc
See more jobs at Mind Ware Incexternal

Other

Posted 25 days ago

Key responsibilities

  • Design and build AI-powered applications using LLMs and foundation models.

  • Develop RAG solutions leveraging enterprise knowledge sources.

  • Design planning, reasoning, tool-calling, and workflow orchestration systems.

Job description

Role: AI Application Engineer / Lead
Location: Santa Clara, CA (Onsite) 

Job description:

  • AI Application Engineer / Lead
  • Role Requirements & Hiring Criteria

  • Location

    Experience

    Priority

    Santa Clara, CA (Onsite)

    5–8 yrs SWE; 3+ yrs AI/ML; 1–2 yrs GenAI

    Production AI / Agentic AI

  • 1. Business Objectives & Expected Outcomes
  • Business Objectives
  • Build enterprise-grade AI applications that improve engineering, R&D, manufacturing, and knowledge management workflows.
  • Accelerate adoption of Agentic AI across Applied Materials.
  • Establish reusable AI platform components and frameworks.
  • Reduce development effort through AI-assisted workflows and reusable services.
  •  
  • Expected Outcomes
  • Deploy production AI applications used by multiple business units.
  • Deliver measurable productivity improvements.
  • Create reusable RAG, agent, and orchestration frameworks.
  • Improve knowledge discovery and decision support across engineering teams.
  •  
  • 2. Detailed Job Description & Key Responsibilities
  • AI Application Development
  • Design and build AI-powered applications using LLMs and foundation models.
  • Develop RAG solutions leveraging enterprise knowledge sources.
  • Build multi-agent systems for complex workflows.
  •  
  • Agentic AI
  • Design planning, reasoning, tool-calling, and workflow orchestration systems.
  • Build autonomous and human-in-the-loop agent architectures.
  • Develop domain-specific AI copilots.
  •  
  • AI Engineering
  • Fine-tune, evaluate, and optimize models.
  • Implement prompt engineering and evaluation frameworks.
  • Build API services for AI model consumption.
  •  
  • Leadership
  • Lead technical solution design.
  • Mentor junior engineers.
  • Driving AI engineering best practices.
  • Partner with R&D, product, and business stakeholders.
  • 4. Technical Stack, Frameworks & Programming Languages

  • Category

    Required / Preferred Stack

    Programming Languages — Mandatory

    Python; SQL

    Programming Languages — Preferred

    TypeScript; JavaScript; C++

    AI Frameworks

    PyTorch; Hugging Face Transformers; TensorFlow; MLflow

    Agent Frameworks

    LangGraph; LangChain; Semantic Kernel; AutoGen

    Vector Databases

    Azure AI Search; Elasticsearch/OpenSearch; Chroma; PGVector

    Backend

    FastAPI; REST APIs; gRPC (preferred)

    Data Platforms

    Databricks; Fabric; PostgreSQL

  •  
  • 5. Cloud Environment
  • Primary
  • • Microsoft Azure / AWS
  •  
  • Services
  • Azure AI Foundry
  • Azure OpenAI
  • Azure AI Search
  • Azure Functions
  • Azure Kubernetes Service (AKS)
  • ADLS Gen2
  • Preferred Additional Experience
  • AWS
  • Google Cloud Platform
  •  
  • 6. Security, Compliance & Data Classification
  • Mandatory
  • Understanding of enterprise security controls.
  • Experience handling Internal and Confidential data.
  • Secure API design.
  • RBAC and identity management.
  •  
  • Preferred
  • Responsible AI implementation.
  • Data governance frameworks.
  • Model monitoring and auditability.
  • PII protection and redaction.
  • AI risk assessment and guardrails.
  •  
  • 7. Expected Deliverables & Success Criteria
  • First 6 Months
  • 1–2 production AI applications.
  • Enterprise RAG framework.
  • Agent orchestration framework.
  • Evaluation and observability dashboards.
  •  
  • First 12 Months
  • Multiple production deployments.
  • Reusable AI platform components.
  • Reduced deployment time and development effort.
  • Adoption across multiple teams.
  • Success Metrics
  • User adoption.
  • Productivity impact.
  • Response quality.
  • Hallucination reduction.
  • Platform reusability.
  • Deployment velocity.
  •  
  • 10. Required Years of Experience
  • Mandatory
  •  
  • 5–8 years Software Engineering
  • 3+ years AI/ML Engineering
  • 1–2 years Generative AI
  •  
  • Preferred
  • 2+ years building production GenAI systems.
  • Experience leading technical workstreams.
  •  
  • 11. Mandatory vs Preferred Skills
  • Mandatory
  • Python
  • LLM application development
  • RAG architecture design
  • PyTorch or TensorFlow
  • REST APIs
  • Azure cloud
  • Vector databases
  • AI evaluation techniques
  • Preferred
  • Multi-agent systems
  • Scientific AI
  • Model fine-tuning
  • Multimodal AI
  • MLOps
  • Databricks
  • Kubernetes
  • MCP ecosystem
  •  
  • Certifications (Preferred)
  • Azure AI Engineer Associate
  • Azure Solutions Architect
  • Databricks ML Professional
  • AWS ML Specialty
  •  
  • 12. Prior Experience with Agentic AI, LLMs & Production Deployments
  • Mandatory Experience — LLMs
  • GPT-family models
  • Claude
  • Llama
  • Mistral
  • Gemini
  • RAG
  • Chunking strategies
  • Embedding generation
  • Hybrid retrieval
  • Reranking
  • Evaluation methodologies
  • Agentic AI
  • Tool calling
  • Function calling
  • Workflow automation
  • Memory management
  • Planning and execution frameworks
  • AI Orchestration Frameworks — Experience with at least one
  • LangGraph
  • Semantic Kernel
  • AutoGen
  • CrewAI
  • Production Deployment
  • CI/CD for AI applications
  • Monitoring and observability
  • Prompt versioning
  • Model lifecycle management
  • Cost optimization

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