AI Engineer

Role & Responsibilities

Conversational AI & Chatbot Development

• Design and implement intelligent chatbot solutions using modern LLMs like GPT-4, Claude, LLaMA, etc.
• Build prompt strategies, chains, and RAG (Retrieval-Augmented Generation) pipelines for enhanced context-aware responses.
• Integrate chatbot systems with APIs, databases, and enterprise applications.
• Optimize model usage to balance cost, latency, and accuracy.
• Develop fallback logic, session tracking, and context management for multi-turn conversations.
• Ensure secure, ethical, and responsible AI usage.
• AI/ML for Predictive Analytics
• Analyze historical and real-time data to identify trends, anomalies, and patterns.
• Work with structured and unstructured data including text, time series, and tabular data.
• Present actionable insights from models to stakeholders in clear and explainable ways.

Architecture & Deployment

• Design and maintain scalable AI/ML pipelines and architecture.
• Use MLOps tools to automate model training, testing, deployment, and monitoring (e.g., MLflow, DVC, Airflow, Kubeflow).
• Integrate models into production-grade applications using APIs and cloud ML services.

Qualifications

• 4 years of relevant experience in AI Engineering.
• Strong experience with LLM-based chatbot frameworks: LangChain, LlamaIndex, Langgraph, etc.
• Expertise in prompt engineering, embeddings, and transformer-based models.
• Proficiency in Python (preferred) or other languages for ML and scripting.
• Familiarity with data pipelines, feature stores, and ETL processes.
• Experience with vector databases (e.g., Pinecone, FAISS, Weaviate).
• Strong understanding of machine learning algorithms, statistics, and data modeling.