Conversational AI Platform Engineer

Job Description : We are seeking an experienced AI Engineer with a strong background in Natural Language Understanding (NLU) who is passionate about pushing the boundaries of Conversational AI. In this role, you will design, develop, and deploy scalable AI solutions leveraging LLMs, Retrieval-Augmented Generation (RAG), and prompt engineering techniques to power intelligent products and services.

As part of our ML / AI team, you’ll own the full lifecycle of model development — from data preparation and fine-tuning to inference optimization and deployment in production environments.

Responsibilities :

Design, fine-tune, and deploy LLM-based applications for Conversational AI use cases

Build scalable retrieval-augmented generation (RAG) pipelines that combine LLMs with structured / unstructured data sources

Develop prompt engineering strategies, templates, and evaluation frameworks for LLM-driven workflows

Collaborate with cross-functional teams to identify and implement AI-driven solutions to business problems

Optimize models for low-latency inference using quantization, distillation, and other model optimization techniques (e.G., ONNX, TensorRT)

Build robust data processing, labeling, and augmentation pipelines to improve model performance

Implement monitoring and evaluation systems for deployed LLMs, ensuring reliability, fairness, and safety

Stay current with emerging trends in LLMs, retrieval systems, and generative AI frameworks

Requirements :

2-4 years of hands-on experience in NLP / LLM.

Strong proficiency in Python and PyTorch and related frameworks (like Hugging Face Transformers, Sentence Transformers etc.)

Proven experience developing and deploying NLP or LLM pipelines in production environments at scale

Solid understanding of transformer architectures and attention mechanisms

Proficiency in using LLM provider APIs such as OpenAI, Gemini etc.Including prompt design, fine-tuning, and evaluation

Experience with model optimization techniques such as quantization, pruning, ONNX, TensorRT, or model distillation

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field

Nice to Have :

Hands-on experience with RAG and vector databases (e.G., FAISS, Qdrant, pgVector etc. )

Prior work on LLM fine-tuning, alignment, or evaluation

Experience with LLM orchestration frameworks such as LlamaIndex or similar tools

Familiarity with multi-provider LLM orchestration, integrating APIs from OpenAI, Gemini etc. and others for fallbacks, routing, or ensemble strategies

Knowledge of MLOps for LLMs, including model serving and monitoring

Understanding of embedding models, context management, and token optimization for scalable LLM applications