Job description

Title: LLM Engineer
Location: Remote - but travel would require whenever needed
Duration: 6 months + possible extension
Number of interviews - 2 (1 Internal and 1 Client Interview)

Must have Skills :

8 years – 10 years, LLM Engineer, Generative AI Fundamentals (Strong) 2 years, Data Science background must- General Experience (5 years), Python (Strong/ intermediate), REST, AWS basic understanding would also work.

Job Description :

An ideal candidate has extensive hands-on python experience with a heavy focus and curiosity in GenAI over the last 2 years.
6+ Years experience building RESTful APIs on Python, with SDKs like Fast API (ideal), Flask, etc.
Experience scaling GenAI solutions to thousands of users in production is ideal.
Experience with RAG, understands of decisions made for the chunking strategy.
Experience with cloud deployments of services (lambda, ecs, or eks) related to GenAI solutions.
Understanding of, ideally experience with, unstructured.io, llama index, Lang chain, and other mainstream RAG components.
Understanding of knowledge graphs and graph RAG, and how to leverage LLMs to create and manage.
Understanding of retrieval mechanisms like vector dbs. and search.
Understanding of agentic workflows.

Other Qualifications:

They are consistently curious about the GenAI space, experimenting and exploring.
Must be comfortable working as part of the global dev team spanning EST, IST, and time zones in between

Role Description:

Data Scientist with 6+ years of expertise in Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI (GenAI).
The ideal candidate will have hands-on experience with search-related work, including relevance tuning, text classification, and topic modeling.
Additionally, experience in building Retrieval-Augmented Generation (RAG) pipelines for search and chat applications is highly desired.

Key Responsibilities:

Develop and optimize NLP models for text classification, text clustering, topic modeling, and relevance tuning in search.
Work with LLMs to build advanced generative AI solutions for search and chat applications.
Design and implement Retrieval-Augmented Generation (RAG) pipelines to improve search and conversational AI systems.
Collaborate with cross-functional teams to deploy data-driven search enhancements and GenAI solutions.
Analyze and fine-tune search relevance based on user behavior and search intent from query log.

Qualifications: