AI Engineer

We are looking for a passionate and proactive Junior AI Engineer – Conversational AI & RAG Systems with practical experience to join our AI development team. The ideal candidate should be eager to learn, experiment, and contribute to building intelligent conversational systems, RAG pipelines, and AI-powered customer service automation. You will assist in developing chatbots, managing vector databases, integrating LLMs, and optimizing real-time AI responses.

Key Responsibilities:

• Assist in the development, testing, and deployment of AI chatbots using local and cloud-based LLMs.
• Support in building RAG (Retrieval-Augmented Generation) systems, including vector search and semantic retrieval.
• Work with Qdrant and embedding models to manage collections, vectors, and similarity search.
• Help develop FastAPI endpoints for AI services and WhatsApp/other messaging integrations.
• Participate in processing training data, embeddings, and conversation history pipelines.
• Implement and refine LLM-based intent detection, response ranking, and fallback systems.
• Collaborate on model optimization, prompt tuning, and improving response accuracy.
• Assist in deploying real-time AI systems for customer service automation.
• Contribute to knowledge base creation, FAQ structuring, and dynamic response generation.
• Write clean, maintainable, and well-documented code for AI modules.
• Stay updated on conversational AI, RAG methodologies, LLM improvements, and vector database innovations.
• Support integrating AI services into cloud environments and multi-tenant architectures.

Required Skills & Qualifications:

• Bachelor’s degree in Computer Science, AI, Data Science, or a related field.
• 0.5 – 2 years of experience working on AI/ML or chatbot projects.
• Hands-on experience with LLMs (OpenAI, Ollama, Hugging Face) and prompt engineering.
• Understanding of vector databases such as Qdrant, Pinecone, or ChromaDB.
• Experience working with embedding models, semantic search, or RAG pipelines.
• Proficiency in Python, especially FastAPI, async workflows, and API development.
• Familiarity with supervised/unsupervised ML concepts and evaluation techniques.
• Understanding of REST APIs, Git, and version control workflows.
• Good analytical, debugging, and problem-solving skills.
• Ability to work in a collaborative, fast-paced engineering environment.
• Exposure to cloud platforms (AWS/GCP/Azure) for deploying AI applications.
• Basic understanding of MLOps, retraining pipelines, or model monitoring.

Job Type: Full-time

Work Location: In person