AI Architect
Job in
Torrance, Los Angeles County, California, 90501, USA
Listed on 2026-06-03
Listing for:
SolGenie
Full Time
position Listed on 2026-06-03
Job specializations:
-
IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Job Description & How to Apply Below
Architect @ Torrance, CA (Onsite) with one of our clients. The detail of the position is as given below.
Title:
GenAI Architect / AI Architect (LLM, Prompt Engineering, RAG, AWS/Azure)
Location:
Torrance, CA (Onsite)
Duration: 6+ Months
Consultants local to CA preferred
Job Summary
We are looking for a hands-on Architect GenAI Engineer / AI Architect to design and implement real-world Generative AI solutions across enterprise use cases. This role requires strong experience in LLMs, prompt engineering, RAG pipelines, and AI-driven automation
, with a focus on POCs → production deployment
.
This is a highly practical role - candidates must have experience building and delivering working AI solutions
, not just theoretical knowledge.
Key Responsibilities:
- Design and implement GenAI solutions using LLMs (OpenAI, Azure OpenAI, Claude, etc.)
- Build and optimize prompt engineering workflows (system prompts, chaining, evaluation)
- Develop RAG pipelines, agents, and context-aware AI applications
- Create and execute POCs and scale them to production environments
- Apply AI for automation, testing, validation, and SDLC optimization
- Analyze business requirements and translate them into AI-driven workflows and test strategies
- Set up and manage AI/ML infrastructure on AWS/Azure (Bedrock, Sage Maker, Azure AI, etc.)
- Implement evaluation frameworks to measure LLM performance (accuracy, hallucination, latency)
- 10+ years of experience with strong hands-on GenAI / LLM implementation
- 2+ years as
Architect (Design and implementation) - Expertise in Prompt Engineering, RAG, Agents, and LLM workflows
- Strong programming skills in Python
- Experience with frameworks/tools like Lang Chain, Llama Index, Lang Graph, OpenAI APIs
- Hands-on experience deploying AI solutions on AWS or Azure
- Experience building end-to-end AI applications (POC → production)
- Strong understanding of AI evaluation techniques and model performance tuning
- Experience applying AI for QA automation, testing, or SDLC optimization
- Familiarity with Selenium, Playwright, or test automation frameworks
- Experience with vector databases (Pinecone, Weaviate, FAISS)
- Exposure to CI/CD pipelines with AI integration
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