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Llamaindex Jobs (NOW HIRING)

Vichara

Senior Development Lead

Brooklyn, NY

Develop retrieval-augmented generation (RAG) pipelines and LLM-based workflows using tools such as LangChain or LlamaIndex Qualifications * Minimum of 8 years of hands-on experience building ...

Vichara

Senior Development Lead

Brooklyn, NY · On-site

Develop retrieval-augmented generation (RAG) pipelines and LLM-based workflows using tools such as LangChain or LlamaIndex Qualifications * Minimum of 8 years of hands-on experience building ...

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Llamaindex information

How does a LlamaIndex engineer typically collaborate with data scientists and other team members during the development of AI-driven applications?

A LlamaIndex engineer often works closely with data scientists, product managers, and software engineers to design and implement robust data pipelines for large language model (LLM) applications. Collaboration usually involves regular meetings to align on data requirements, model performance metrics, and integration points. Engineers are responsible for ensuring data is efficiently indexed, accessible, and up-to-date, while also providing technical insights to optimize retrieval and performance. Effective communication and teamwork are key, as projects are often cross-functional and iterative, requiring ongoing feedback and adaptation.

What is the difference between Llamaindex vs Data Analyst?

AspectLlamaindexData Analyst
Required credentialsTypically requires knowledge of data management, APIs, and AI toolsBachelor's degree in statistics, mathematics, or related field; often requires certifications in data analysis
Work environmentTech companies, AI startups, data-driven organizationsBusiness, finance, healthcare, and other industries with data needs
Employer and industry usageUsed by organizations integrating AI and data indexing solutionsCommon in industries analyzing large datasets for insights
Search and comparison intentUnderstanding AI data tools vs traditional data analysis rolesComparing AI-driven data indexing tools with traditional data analysis

While Llamaindex focuses on AI-powered data management and integration, Data Analysts primarily interpret and analyze data to inform business decisions. Both roles require data literacy but differ in technical focus and work environment.

What is LlamaIndex?

LlamaIndex is an open-source data framework that helps developers connect large language models (LLMs) to various data sources. It provides tools to ingest, organize, and query data, making it easier for LLMs to retrieve relevant information from documents, databases, APIs, and more. LlamaIndex streamlines the development of LLM-powered applications by managing data pipelines and facilitating efficient, context-aware interactions with external or private data. This enables more useful, accurate, and customizable AI solutions for a wide range of use cases.

What are the key skills and qualifications needed to thrive as a LlamaIndex Engineer, and why are they important?

To thrive as a LlamaIndex Engineer, you need strong programming skills in Python, experience with data structures, and a solid understanding of information retrieval concepts, typically supported by a degree in computer science or a related field. Familiarity with LlamaIndex's framework, knowledge of vector databases (like FAISS or Pinecone), and experience with machine learning libraries are commonly required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with teams and building scalable data solutions. These skills ensure the delivery of efficient, reliable systems that support advanced data indexing and retrieval tasks.
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Llamaindex jobs near you
Architect - Gen AI ML - New York / Jersey City

Architect - Gen AI ML - New York / Jersey City

Photon

Irving, TX • On-site

Other

Medical, Dental, Vision, Retirement, PTO

Posted 12 days ago

Job description

Role Summary:
We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.

Technical Stack:

LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.

Frameworks: LangChain, LlamaIndex, and Hugging Face.

Orchestration: LangGraph and Multi-Agent Systems (MAS).

Development: Python, FastAPI, and Asynchronous Programming.

RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.

ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.

Deployment: Docker, Production API management, and LLM monitoring.

Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.

Key Responsibilities:

Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.

Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.

Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.

Design and deploy high-performance, scalable backend services using FastAPI and Async Python.

Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.

Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.

Containerize and deploy AI services via Docker to production environments.

Required Qualifications:

Hands-on experience building and deploying GenAI applications in a production setting.

Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).

Experience with vector search, embedding models, and advanced data retrieval patterns.

Knowledge of model fine-tuning techniques and local LLM quantization/hosting.

Familiarity with production-grade monitoring, API security, and CI/CD for ML.

Compensation, Benefits and Duration

Minimum Compensation: USD 79,000
Maximum Compensation: USD 276,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

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