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
2-4 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.

Education- Bachelors/Masters degree in Computer Science, Data Science, AI/ML