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Llm Ml Rag Jobs in Florida (NOW HIRING)

Bomnin Chevrolet Dadeland

Sr. AI/ML Engineer (LLM)

Miami, FL · On-site

$99K - $137K/yr

Create and architect interpreters, Agented Systems, and integrate multi-hop RAG and other LLM ... Provide AI/ML technical leadership and mentorship to other engineers on the team. * Ensure that LLM ...

Accenture Federal Services

AI/ML Engineer

Tampa, FL · Hybrid

Design, develop, and maintain RAG pipelines, including document ingestion, embedding generation ... and LLM orchestration. * Build and optimize LLMpowered applications for classification ...

Vultus Inc

AI/ML Engineer

Miami, FL · On-site +1

$120K - $150K/yr

Build and optimize Generative AI and LLM-based applications. * Fine-tune foundation models for ... Develop Retrieval-Augmented Generation (RAG) pipelines. * Work with vector databases for semantic ...

Accenture Federal Services

AI/ML Engineer

Tampa, FL · On-site

$108K - $185K/yr

Design, develop, and maintain RAG pipelines, including document ingestion, embedding generation ... Build and optimize LLM-powered applications for classification, summarization, Q&A, knowledge ...

Accenture Federal Services

AI/ML Engineer

Tampa, FL · Hybrid

$108K - $185K/yr

Design, develop, and maintain RAG pipelines, including document ingestion, embedding generation ... LLM orchestration. * Build and optimize LLM‑powered applications for classification ...

Elevance Health

Gen AI Engineer

Lake Mary, FL · Hybrid

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

Elevance Health

Gen AI Engineer

Lake Mary, FL · Hybrid

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

WWC Global LLC

Machine Learning (ML) Engineer

Tampa, FL · On-site

Developing AI/ML algorithms and models including those designed for Natural Language Processing ... Ensuring all LLM/RAG experimentation and model development adheres to mandatory security and data ...

CS Solutions, Inc.

GenAI Technical Architect

Miami, FL · On-site

$63.25 - $76.50/hr

... RAG, MCP connector, agent orchestration, APIs, Snowflake, Graph DB, AI/ML, LlamaIndex, LangChain ... MCP / tool-layer design for LLM agents; FASTMCP a strong plus * Financial services platforms ...

Brillius

AI Architect

Miami, FL · On-site

Proven track record architecting RAG systems, vector search, and LLM-based knowledge platforms. * Strong hands-on experience with: o Python o Cloud ML platforms-Azure o Vector DBs (Pinecone, DB ...

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Llm Ml Rag information

What are some typical challenges faced when working on Retrieval-Augmented Generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as an LLM ML RAG (Retrieval-Augmented Generation) Engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What are LLM ML RAG jobs?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.
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Llm Ml Rag jobs near you

Sr. AI/ML Engineer (LLM)

Bomnin Automotive Group

Miami, FL

Full-time

Re-posted 25 days ago

Job description

Job description

Company Description

Dealer Automation Technologies is a leading Information Technology Services company that provides Software as a Service (SaaS) solutions for the automotive industry. The company specializes in Process Automation, Advanced Analytics, and Augmented Intelligence to redefine dealership operations. With a focus on intuitive user experiences and cutting-edge technologies, Dealer Automation Technologies is paving the way for innovation in the sector. Combining the agility of a startup with the expertise and business acumen of seasoned leaders, the company operates without dependence on legacy systems, fostering a dynamic and innovative work culture.

Role Description

This is a full-time, on-site role located in Miami, FL, for a Senior AI/ML Engineer specializing in Large Language Models (LLMs) to join our team. You will play a key role in designing and implementing workflows that leverage large language models (LLMs, LAMs, LMMs, LVLMs, etc.) to automate processes and drive innovation in our products. The ideal candidate will have a deep understanding of NLP, experience with foundational models, and a flexible, problem-solving mindset. You will collaborate closely with cross-functional teams, contributing to the development of scalable AI driven solutions. Other primary responsibilities include designing and implementing machine learning models, particularly in natural language processing and large language models, building scalable algorithms, conducting research on neural networks, and evaluating model performance. Additionally, the engineer will collaborate with cross-functional teams to ensure seamless integration of AI/ML components into the company’s software offerings.

Major Areas of Responsibility

  • Design, Implement, and optimize workflows that incorporate large language models to automate and enhance product features.
  • Leverage existing foundational models and adapt them to fit into various product requirements, ensuring alignment with business goals.
  • Collaborate with product managers, data scientist, and software engineers to integrate LLM-based automation into scalable solutions.
  • Create and architect interpreters, Agented Systems, and integrate multi-hop RAG and other LLM experiences into existing systems to coordinate knowledge responses.
  • Research and evaluate new technologies and methodologies in the LLM space to continuously improve product automation.
  • Work on the customization and fine-tunning of models to optimize performance for specific use cases.
  • Develop, test, and deploy LLM-based services in production environments.
  • Provide AI/ML technical leadership and mentorship to other engineers on the team.
  • Ensure that LLM integrations are efficient, scalable, and secure, adhering to industry best practices.

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