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LLM Engineer - (Hybrid- Greenfield AI Project)
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LLM Engineer - (Hybrid- Greenfield AI Project)
Match Made Tech —Los Angeles, CA
Partner with engineering teams (front-end, back-end, and data), designers, and product leads to define use cases and success criteria.
$75 - $95 an hour
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LLM Engineer - (Hybrid- Greenfield AI Project)

Match Made Tech
Los Angeles, CA

Quick Apply

Job Details

Full-time
$75 - $95 an hour

Qualifications

AI models
Application deployment
Prompt engineering
Production systems
Model deployment
Python

Full Job Description

Job Title: LLM Engineer - Greenfield AI Project

LOCATION: Irvine, CA (onsite). Monday through Thursday onsite, Fridays remote.

MUST BE A US CITIZEN/ GREENCARD HOLDER

COMPENSATION: $75-95 an hour. This is a 2-year contract that will convert to full-time.

ABOUT US: We are on a mission to develop innovative AI solutions that will revolutionize our workforce. As we embark on an exciting new greenfield AI project, we are seeking an exceptional LLM Engineer to join our team and lead the development of machine learning models as part of this groundbreaking initiative.

JOB DESCRIPTION:

We are looking for an experienced Generative AI/LLM Engineer to lead the development and deployment of applications powered by large language models (LLMs). You’ll design and implement generative AI systems - including intelligent chatbots, agentic AI frameworks, RAG pipelines, and multimodal experiences - that leverage cutting-edge advances in NLP and transformer-based architecture.

Key Responsibilities

LLM Application Design & Development
- Build custom pipelines for RAG, embeddings, prompt tuning, and chain-of-thought reasoning
- Design agentic workflows using frameworks like OpenAI’s Agents SDK or LangChain.
Prompt Engineering & Evaluation
- Craft robust prompts for diverse tasks and use few-shot/prompt-chaining techniques
- Evaluate LLM output quality, safety, and latency through automated and human feedback loops
Model Integration & Scaling
- Integrate LLMs with internal systems, APIs, relational databases, NoSQL databases, vector databases, and real-time inference stacks
- Ensure scalable and efficient deployment using cloud-native tools (e.g., OpenAI, Azure OpenAI, AWS SageMaker, or Google Vertex AI)
Innovation & Research
- Explore and implement multi-modal AI integrations (text, image, voice)
- Stay current with LLM advancements, open-source model benchmarks, and academic research
Collaboration & DevOps
- Partner with engineering teams (front-end, back-end, and data), designers, and product leads to define use cases and success criteria
- Write clean, testable code and participate in architecture reviews and Agile ceremonies

Qualifications & Skills

1–3 years of experience developing generative AI solutions in NLP, chatbot development, or content generation (text or image)
3–5 years of hands-on experience in AI or machine learning engineering
Deep understanding of transformer architectures, attention mechanisms, and tokenization
Experience deploying LLM-based solutions into production and working with APIs (OpenAI, Anthropic, Cohere, etc.)
Proficiency in Python, with knowledge of tools like Hugging Face Transformers, LangChain, or OpenLLM
Hands-on experience with vector databases (Pinecone, Weaviate, FAISS) for managing and querying embeddings
Strong understanding of prompt engineering, RAG, agent frameworks, and function tooling for integrating LLMs with external APIs and data sources
Familiarity with distributed computing, GPU acceleration, and inference optimization
Bachelor’s degree in Computer Science, Engineering, or a related field

Preferred Qualifications (Nice-to-Have)

Master’s or PhD degree in Computer Science, Engineering, or a related field
Experience with multi-modal AI systems (e.g., combining text, image, speech)
Knowledge of responsible AI practices and safety alignment in generative AI
Familiarity with open-source LLMs (Mistral, Mixtral, Falcon, LLaMA)
Understanding of the Model Context Protocol (MCP) and its integration in LLM ecosystems
Background in conversational design, dialogue management, and human-in-the-loop workflows

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