Senior AI Engineer - AI Agents / RAG / Fine-Tuning

About the Role We are looking for experienced AI Engineer with hands-on experience in AI agents, Retrieval-Augmented Generation (RAG), and model fine-tuning. In this role, you will design, build, and optimize intelligent systems that enhance automation, knowledge retrieval, and decision-making across our product ecosystem. You will collaborate closely with product, engineering, and data teams to deliver scalable, production-grade AI solutions. Key Responsibilities Design, develop, and deploy AI agent systems capable of task planning, tool usage, and autonomous workflow execution. Build and optimize RAG pipelines, including document chunking, embeddings, vector search, and retrieval orchestration. Fine-tune large language models (LLMs) using instruction tuning, supervised fine-tuning (SFT), or reinforcement learning from human feedback (RLHF). Implement high-performance inference pipelines and monitor model performance in production. Collaborate with cross-functional teams to integrate AI services into products and internal platforms.

Basic Qualifications Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field. 4–6 years of hands-on experience in machine learning or NLP engineering roles. Strong proficiency in Python and AI/ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face). Proven experience in: Building and deploying AI agents (LangChain, AutoGen, OpenAI Assistants, etc.) Designing RAG pipelines with vector databases (e.g., Pinecone, FAISS, Weaviate, Milvus) Fine-tuning LLMs on custom datasets Solid understanding of NLP concepts, embeddings, prompt engineering, and model evaluation. Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is a plus. Strong problem-solving skills, communication ability, and a passion for applied AI innovation.

Preferred Qualifications Experience with multi-agent systems or agent frameworks. Knowledge of distributed systems and GPU optimization. Familiarity with MLOps tools (Weights & Biases, MLflow, Ray, etc.). Background in dataset curation and synthetic data generation.