AI Engineer

Overview

We are looking for an AI Engineer to design, build, and optimize AI capabilities that power our products and internal systems. This role focuses heavily on Generative AI, NLP, and end-to-end LLM integration, with some responsibilities in data analysis to support model evaluation and product insights.

Key Responsibilities

LLM / AI Development

• Design, develop, and optimize LLM solutions using models such as OpenAI, Anthropic, Llama, Mistral, or HuggingFace ecosystem.
• Create effective prompting strategies and develop prompt engineering best practices.
• Build and refine Retrieval-Augmented Generation (RAG) pipelines.
• Fine-tune or adapt models using LoRA/PEFT where necessary.
• Implement pipelines for inference, evaluation, benchmarking, and model selection.
• Work closely with Product and Engineering teams to integrate LLM capabilities into real applications.

AI System Integration

• Develop and maintain inference services and APIs for LLM features.
• Collaborate with software engineers to integrate AI functionality into existing systems.
• Optimize models and pipelines for performance, scalability, latency, and cost.

Data Science Responsibilities

• Perform exploratory data analysis to support model understanding and decision-making.
• Build supporting ML models (e.g., classification, clustering) when required for LLM use cases.
• Define and implement evaluation metrics, scoring rubrics, and A/B testing strategies for LLM performance.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related fields.
• Strong proficiency in Python and AI/ML frameworks such as HuggingFace Transformers, PyTorch, LangChain/LlamaIndex.
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI, Google Gemini, etc.).
• Solid understanding of NLP concepts, prompt engineering, RAG, and vector databases (FAISS, Pinecone, Weaviate, etc.).
• Strong software engineering fundamentals and ability to build production-ready systems.
• Ability to design experiments, evaluate model performance, and present clear conclusions.

Preferred Qualifications

• Experience fine-tuning or customizing LLMs using PEFT/LoRA.
• Experience deploying AI systems on cloud platforms such as AWS, GCP, or Azure.
• Familiarity with MLOps tools like MLflow, Weights & Biases, or similar.
• Experience building chatbots, agentic workflows, or automated AI pipelines.