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Remote Python Ai Jobs in Chula Vista, CA (NOW HIRING)

TEKsystems

AI Engineer

San Diego, CA · Remote

$50 - $58/hr

Remote AI/ML Software Engineer, Generative AI Are you passionate about building Generative AI ... Strong Python development experience. * Production experience with Google Gemini and Vertex AI.

AbbVie

Machine Learning Engineer

San Diego, CA · On-site +1

... and AI solutions * Design, train, evaluate, and refine machine learning models with minimal ... Strong programming skills in Python and solid understanding of core computer scienceprinciples

Arctiq

Senior Site Reliability Engineer

San Diego, CA · Remote

$58.25 - $77.50/hr

... Data & AI, Autonomous Operations & Intelligence, and Enterprise Service Management. We help ... This is a remote, contract opportunity for a project Arctiq is delivering for a client. Candidates ...

Arctiq, Inc.

Senior Site Reliability Engineer

San Diego, CA · Remote

$60.50 - $80.50/hr

... Data & AI, Autonomous Operations & Intelligence, and Enterprise Service Management. We help ... This is a remote, contract opportunity for a project Arctiq is delivering for a client. Candidates ...

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All JobsPython Ai JobsRemote Python Ai JobsRemote Python Ai Jobs in Chula Vista, CA

Remote Python Ai information

See Chula Vista, CA salary details

$13

$60

$89

How much do remote python ai jobs pay per hour?

As of Aug 27, 2026, the average hourly pay for remote python ai in Chula Vista, CA is $60.77, according to ZipRecruiter salary data. Most workers in this role earn between $50.10 and $69.04 per hour, depending on experience, location, and employer.

What is a remote Python AI job?

Remote Python AI jobs are positions that involve developing, implementing, or maintaining artificial intelligence solutions using the Python programming language, all while working from a remote location. These roles can include tasks such as building machine learning models, automating data analysis, and deploying AI-powered applications. Professionals in these jobs collaborate with teams online, use cloud-based tools, and contribute to a variety of industries such as tech, finance, healthcare, and more. Python is a popular choice for AI due to its simplicity and the availability of powerful libraries like TensorFlow, PyTorch, and scikit-learn.

What skills and qualifications are needed to thrive as a remote Python AI developer?

To thrive as a Remote Python AI Developer, you need strong programming skills in Python, a solid understanding of machine learning concepts, and typically a degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with cloud platforms (e.g., AWS, Azure), and relevant certifications such as TensorFlow Developer are highly valued. Exceptional problem-solving abilities, self-motivation, and effective remote communication skills help set professionals apart in this distributed role. These capabilities are critical for developing robust AI solutions, collaborating across virtual teams, and delivering impactful results in a rapidly evolving field.

How is collaboration typically structured in a remote Python AI role, and what tools are commonly used to facilitate teamwork?

In a remote Python AI role, collaboration is often structured through regular virtual meetings, code reviews, and the use of collaborative platforms. Teams typically use version control systems like GitHub or GitLab for code sharing, and platforms such as Slack or Microsoft Teams for daily communication. Project management tools like Jira or Trello help organize tasks and track progress, while video calls via Zoom or Google Meet are used for team discussions and brainstorming sessions. This structure ensures that even in a distributed setting, team members can efficiently work together, share insights, and resolve challenges.

What is the difference between Remote Python Ai vs Data Scientist?

AspectRemote Python AiData Scientist
Required CredentialsPython programming, AI/ML knowledge, possibly certifications in AI or data analysisStatistics, programming, data analysis, often a master's degree or higher
Work EnvironmentRemote, tech companies, AI-focused teamsRemote or on-site, diverse industries including tech, finance, healthcare
Employer & Industry UsageTech startups, AI firms, software companiesVarious sectors like finance, healthcare, marketing, tech
Search & Comparison IntentFocus on AI development using PythonData analysis, insights, statistical modeling

Remote Python Ai roles primarily focus on developing AI models and applications using Python, often within tech or AI companies. Data Scientists analyze data to extract insights, requiring broader statistical skills. While both roles may involve Python, Remote Python Ai emphasizes AI/ML development, whereas Data Scientists focus on data analysis and interpretation.

What are popular job titles related to Remote Python Ai jobs in Chula Vista, CA?

For Remote Python Ai jobs in Chula Vista, CA, the most frequently searched job titles are:

What job categories do people searching Remote Python Ai jobs in Chula Vista, CA look for?

The top searched job categories for Remote Python Ai jobs in Chula Vista, CA are:

What cities near Chula Vista, CA are hiring for Remote Python Ai jobs?

Cities near Chula Vista, CA with the most Remote Python Ai job openings:

Remote Python Ai jobs near you

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.

Apply