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.