AI Engineer

Job Summary

We are seeking an innovative AI Engineer to design, develop, and deploy AI-powered applications and intelligent automation solutions. The ideal candidate will have hands-on experience with Large Language Models (LLMs), machine learning, prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and cloud-based AI services. You will collaborate with product managers, data scientists, software engineers, and UX teams to build scalable AI-driven products that solve real-world business problems.

Key Responsibilities

• Design, develop, and deploy AI-powered applications using modern AI frameworks and tools.
• Build and optimize applications leveraging Large Language Models (LLMs) such as GPT, Claude, Gemini, or open-source models (Llama, Mistral).
• Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
• Design and implement AI agents and multi-agent workflows for task automation.
• Build RESTful APIs and backend services to integrate AI models into applications.
• Fine-tune, evaluate, and optimize AI models for performance, cost, and accuracy.
• Develop prompt engineering strategies and evaluation frameworks.
• Work with structured and unstructured data to build intelligent AI solutions.
• Collaborate with cross-functional teams to translate business requirements into AI capabilities.
• Monitor AI systems in production and continuously improve reliability, scalability, and security.
• Stay updated with the latest advancements in Generative AI, machine learning, and AI infrastructure.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3-7 years of software engineering or AI/ML development experience.
• Strong proficiency in Python.
• Experience building applications using LLM APIs (OpenAI, Anthropic, Google Gemini, Azure OpenAI, etc.).
• Hands-on experience with frameworks such as:
  • LangChain
  • LlamaIndex
  • LangGraph
  • Haystack (preferred)
• Experience implementing RAG systems.
• Knowledge of prompt engineering techniques and LLM evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or Milvus.
• Strong understanding of REST APIs and backend development.
• Experience with SQL and NoSQL databases.
• Familiarity with Git and Agile development practices.