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kpg99 inc

Title: LLM Engineer

Location Offshore Remote India (IST hours with Some Overlap in US EST Time)

Duration 12+ Months

 

 

Must Have:-

Python , LangChain , AWS, RAG, (FastAPI OR Flask)

 

Role Overview

We are seeking a skilled LLM Engineer proficient in Python programming and experienced in developing, deploying, and optimizing large language models (LLMs). The ideal candidate will have hands-on experience with FastAPI or Flask frameworks, Lang Chain implementation, and building Retrieval-Augmented Generation (RAG) pipelines. You will play a key role in integrating cutting-edge AI technologies to solve complex business problems, focusing on vector stores and retrievers while deploying scalable solutions on AWS.

 

Key Responsibilities

1. Python Development:
2. Design, develop, and maintain scalable web services using FastAPI or Flask frameworks.
3. Write efficient, reusable, and modular Python code to support API-driven LLM applications.
4. Lang Chain & Supporting Frameworks:
5. Implement Lang Chain to build custom pipelines for document indexing, retrieval, and summarization.
6. Integrate Lang Chain’s RAG capabilities with other components like vector stores and retrievers to support real-time querying and document processing.
7. RAG Pipelines:
8. Architect and deploy Retrieval-Augmented Generation (RAG) systems for chatbots, knowledge systems, and other generative AI applications.
9. Optimize RAG systems for speed, accuracy, and scalability across multiple use cases.
10. Vector Stores & Retrievers:
11. Work with vector databases like Pinecone, Chroma, FAISS, or Milvus to store and manage embeddings.
12. Implement retrievers and re-rankers to improve query efficiency, ensuring high-quality and relevant outputs for users.
13. AWS Cloud Deployment:
14. Deploy and manage LLM-based applications on AWS, leveraging services such as Lambda, EC2, S3, EKS, and RDS.
15. Ensure the scalability, availability, and reliability of deployed applications.
16. Dashboards and Monitoring (Optional):
17. Create monitoring dashboards using tools like Grafana or Tableau for real-time system monitoring, analytics, and performance insights.
18. Experimentation with Generative AI:
19. Research and integrate the latest advancements in generative AI technologies.
20. Experiment with fine-tuning and adapting large language models (like GPT, BERT) for new, innovative use cases.

Required Technical Skills

* Python proficiency, especially with web frameworks like FastAPI or Flask.
* Strong experience with Lang Chain and associated libraries.
* Proven expertise in building and optimizing RAG pipelines.
* Proficiency in using vector databases (e.g., Pinecone, FAISS).
* Experience with retrievers and re-rankers.
* Solid understanding of AWS services (Lambda, EC2, RDS, etc.).
* Knowledge of SQL and NoSQL databases.
* Familiarity with dashboarding tools such as Grafana and Tableau.