Description

Responsibilities

• Design and develop AI-native applications using LLMs and Generative AI technologies.
• Build scalable Retrieval-Augmented Generation (RAG) pipelines using vector databases.
• Integrate AI models through OpenAI, Anthropic, Gemini, or similar APIs.
• Develop RESTful APIs and microservices using FastAPI, Flask, or similar frameworks.
• Implement prompt engineering and AI workflow orchestration using LangChain, LangGraph, or LlamaIndex.
• Deploy AI applications using Docker, Kubernetes, and cloud platforms.
• Optimize AI model performance, latency, and operational costs.
• Collaborate with product managers, data scientists, and software engineers to deliver AI-driven solutions.
• Ensure application security, monitoring, and scalability in production.
• Stay updated with the latest advancements in AI, LLMs, and software engineering best practices.

Required Qualifications

• 5+ years of experience in software development.
• Strong programming skills in Python (preferred), Java, or Node.js.
• Hands-on experience with LLMs, Generative AI, and prompt engineering.
• Experience with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.
• Knowledge of RAG architecture and vector databases such as Pinecone, FAISS, Milvus, Weaviate, or Chroma.
• Experience building REST APIs and microservices.
• Familiarity with SQL/NoSQL databases.
• Hands-on experience with Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).
• Strong problem-solving, debugging, and communication skills.

Preferred Skills

• Experience with AI agents and multi-agent systems.
• Knowledge of MLOps and model deployment pipelines.
• Experience with AI observability, evaluation, and monitoring tools.
• Familiarity with DevOps and CI/CD pipelines.
• Exposure to fine-tuning or deploying open-source LLMs (e.g., Llama, Mistral, Qwen)

Key Skills

Education

Bachelor's degree