Job Description

As an AI Engineer, you will be responsible for designing, building, and deploying our cutting-edge Large Language Model systems. You will go beyond API integrations to tackle the hands-on challenges of adapting, fine-tuning, and optimizing open-source models for our specific domain. Your work will be the cornerstone of our AI-driven features, directly impacting our users and our product's capabilities.

Key Responsibilities:

Design & Implementation:

Lead the end-to-end development of our LLM stack, from prototyping to production. This includes fine-tuning state-of-the-art open-source models (like LLaMA, Mistral, etc.) using techniques such as LoRA, QLoRA, and full-parameter fine-tuning.

RAG System Development:

Build and optimize production-grade Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy with our proprietary data.

Model Optimization:

Optimize models for inference, focusing on reducing latency and cost through quantization, pruning, and compilation (e.g., using vLLM, ONNX Runtime).

Data Curation & Engineering:

Build robust data pipelines for collecting, cleaning, and structuring training data for instruction-tuning and alignment.

Evaluation & Experimentation:

Develop rigorous benchmarking frameworks to evaluate model performance, mitigate hallucination, and ensure our systems are robust and reliable.

Production Deployment:

Collaborate with MLOps and backend teams to deploy, monitor, and maintain models in a live cloud environment (AWS, GCP, Azure).

Technical Leadership:

Stay ahead of the curve by researching and implementing the latest advancements from the open-source community and academic papers.

Who You Are:

You are a pragmatic builder with a deep passion for AI. You have a strong foundation in machine learning and are proficient in Python and key ML frameworks. You are not just interested in what models can do, but how they do it, and you enjoy the challenge of making them work efficiently in a real-world setting.

Required Qualifications:

Bachelor's or Master's in Computer Science, AI, or a related field, or equivalent proven experience. 2+ years of hands-on experience in building and deploying machine learning models. Strong proficiency in Python and deep learning frameworks like

PyTorch

or TensorFlow.

Proven experience in fine-tuning and deploying Large Language Models.

Solid understanding of the Transformer architecture and modern NLP. Experience with the Hugging Face ecosystem (Transformers, Datasets, Tokenizers, PEFT). Familiarity with vector databases (e.g., Pinecone, Weaviate, pgvector) and RAG concepts. Experience working in a cloud environment and with containerization (Docker, Kubernetes).

Bonus Points (Nice-to-Have):

Experience with LLM inference optimization tools (e.g., vLLM, TensorRT-LLM). Experience with LLM evaluation frameworks and benchmarks (HELM, MT-Bench). Knowledge of reinforcement learning from human feedback (RLHF) or direct preference optimization (DPO). Contributions to open-source AI projects or a strong portfolio of personal projects. Publications in relevant ML venues.

What We Offer:

A competitive salary and equity package. The opportunity to work on foundational AI technology with a high degree of ownership. Access to state-of-the-art hardware (GPUs) and computational resources. A collaborative, fast-paced environment with a team that values excellence.
Job Types: Full-time, Permanent

Pay: ₹276,373.27 - ₹1,520,994.66 per year

Work Location: In person