About the job

Role Overview

Build and deploy generative AI solutions using LLM fine-tuning, RAG architectures, and agentic AI systems.

Responsibilities

Fine-tune large language models for domain-specific applications and use cases

Design and implement RAG (Retrieval Augmented Generation) pipelines with vector databases

Build agentic AI systems with autonomous decision-making and tool-using capabilities

Optimize LLM performance through prompt engineering, parameter tuning, and model selection

Develop and deploy production-ready GenAI applications and APIs

Evaluate model performance, conduct A/B testing, and iterate on solutions

Collaborate with cross-functional teams to integrate GenAI into products

Requirements

Bachelor's degree in Computer Science, Engineering, AI/ML, or related field

Strong experience with LLM fine-tuning (LoRA, QLoRA, full fine-tuning, PEFT methods)

Proven expertise building RAG systems with vector databases and embeddings

Hands-on experience developing agentic AI solutions and multi-agent systems

Proficiency with LLM frameworks (LangChain, LlamaIndex, Haystack)

Experience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS)

Strong Python programming skills

Understanding of transformer architectures and LLM APIs

Preferred

Experience with distributed training and GPU optimization

Knowledge of MLOps, model deployment, and monitoring

Familiarity with Hugging Face ecosystem

Experience with prompt optimization and evaluation frameworks

Understanding of LLM safety, alignment, and guardrails

Background in NLP or deep learning research