AI Engineer

Role: AI Engineer / Generative AI Engineer

Responsibilities:

Design, develop, and deploy AI/ML solutions for real-world business problems.

Build and integrate Generative AI applications using LLMs such as GPT, Claude, Gemini, and open-source models.

Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and semantic search.

Work with Python, FastAPI, LangChain/LangGraph, and AI/ML frameworks.

Integrate AI models into existing Node.js, React, and backend applications through APIs.

Develop AI-powered chatbots, document processing systems, recommendation systems, and automation workflows.

Implement prompt engineering, structured outputs, function/tool calling, and agentic workflows.

Work with vector databases such as Pinecone, Qdrant, Weaviate, or pgvector.

Prepare, clean, and process datasets for AI/ML applications.

Evaluate LLM responses for accuracy, relevance, latency, and cost.

Optimize AI applications for production, including caching, scalability, monitoring, and security.

Deploy AI services using AWS, Docker, CI/CD, and cloud infrastructure.

Collaborate with product and engineering teams to convert business requirements into AI solutions.

Required Skills

Programming: Python, JavaScript/TypeScript, SQL

AI/ML: Machine Learning, Deep Learning, NLP, LLMs, Generative AI

GenAI: RAG, Prompt Engineering, Embeddings, Vector Search, AI Agents, Function Calling

Frameworks: LangChain, LangGraph, FastAPI, PyTorch/TensorFlow

Databases: PostgreSQL, MongoDB, Redis, Vector Databases

Backend: Node.js, Express, REST APIs, Microservices

Cloud/DevOps: AWS, Docker, Git, CI/CD

Other: API integration, system design, model evaluation, AI security