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:
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Design, implement, and optimize end-to-end LLM-based solutions for real-world applications.
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Develop and maintain RAG pipelines integrating vector databases, embeddings, and retrieval techniques.
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Fine-tune pre-trained language models using LoRA or similar methods.
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Apply quantization and optimization strategies to deploy models efficiently on constrained environments.
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Collaborate with data scientists, software engineers, and product teams to integrate AI features into production systems.
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Monitor, evaluate, and continuously improve model performance and reliability.

Required Skills:
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3–5 years of experience in AI/ML development or applied NLP.
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Proficient in Python and frameworks such as PyTorch or TensorFlow.
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Strong understanding of LLM architectures (e.g., GPT, Llama, Falcon, Mistral).
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Experience with RAG frameworks (LangChain, LlamaIndex, or custom retrieval setups).
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Hands-on knowledge of LoRA, PEFT, and model quantization (GPTQ, AWQ, or similar).
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Familiarity with vector databases like FAISS, Pinecone, or ChromaDB.
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Good understanding of prompt engineering and evaluation techniques.
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Cloud deployment experience (AWS, Azure, or GCP) is an advantage.

Preferred Skills :
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Exposure to open‑source models and fine-tuning pipelines.
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Experience integrating AI models into web or enterprise products.
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Knowledge of containerization and MLOps (Docker, Kubernetes, MLflow).