Artificial Intelligence Engineer

Role Overview

We’re looking for an AI Engineer for one of our Tier-1 IT clients with hands-on experience in building, fine-tuning, and optimizing LLM-based applications. The ideal candidate will have solid expertise in RAG (Retrieval-Augmented Generation) architectures, parameter-efficient fine-tuning (e.g., LoRA), and model quantization techniques for deployment efficiency.

Key Responsibilities:

●     Design, implement, and optimize end-to-end LLM-based solutions for real-world applications.

●     Develop and maintain RAG pipelines integrating vector databases, embeddings, and retrieval techniques.

●     Fine-tune pre-trained language models using LoRA or similar methods.

●     Apply quantization and optimization strategies to deploy models efficiently on constrained environments.

●     Collaborate with data scientists, software engineers, and product teams to integrate AI features into production systems.

●     Monitor, evaluate, and continuously improve model performance and reliability.

Required Skills:

●     3–5 years of experience in AI/ML development or applied NLP.

●     Proficient in Python and frameworks such as PyTorch or TensorFlow.

●     Strong understanding of LLM architectures (e.g., GPT, Llama, Falcon, Mistral).

●     Experience with RAG frameworks (LangChain, LlamaIndex, or custom retrieval setups).

●     Hands-on knowledge of LoRA, PEFT, and model quantization (GPTQ, AWQ, or similar).

●     Familiarity with vector databases like FAISS, Pinecone, or ChromaDB.

●     Good understanding of prompt engineering and evaluation techniques.

●     Cloud deployment experience (AWS, Azure, or GCP) is an advantage.

Preferred Skills

●     Exposure to open‑source models and fine-tuning pipelines.

●     Experience integrating AI models into web or enterprise products.

●     Knowledge of containerization and MLOps (Docker, Kubernetes, MLflow).