Generative AI Developer

Job description

• 2 to 7 years of hands-on experience in AI/ML, Generative AI, or related software development. • Strong programming experience in Python and familiarity with AI/ML development frameworks and libraries. • Strong understanding of Generative AI concepts, Large Language Models (LLMs), and Transformer-based architectures. • Hands-on experience with LLMs such as GPT, Azure Open AI or similar open source/commercial models. • Experience building GenAI applications using frameworks such as LangChain, LangGraph, LlamaIndex, or similar frameworks. • Strong understanding of Agentic AI concepts, including AI agents, tool calling, planning, reasoning, memory, and multi-agent workflows. • Experience developing AI agents and integrating external tools, APIs, databases, and enterprise services. • Experience with Retrieval-Augmented Generation (RAG), embeddings, vector databases, semantic search, and document-based question answering. • Understanding of prompt engineering, prompt optimization, structured outputs, function/tool calling, and context management. • Experience with REST API development using FastAPI or similar frameworks. • Experience with ML libraries such as PyTorch, TensorFlow, Scikit-learn, or similar frameworks. • Understanding of machine learning concepts including supervised and unsupervised learning, model evaluation, feature engineering, and model optimization. • Understanding of LLM evaluation, hallucination detection, response quality, and AI application performance optimization. • Experience in prompt engineering with AI-powered developer tools such as GitHub Copilot, Cursor AI, Claude Code, Windsurf, or similar coding assistants. • Experience with AI-assisted development and Vibe Coding practices. • Ability to review, validate, optimize, and secure AI-generated code. • Good understanding of SDLC and Agile development methodologies. Nice to Have Skills • Exposure to modern engineering platforms such as Harness / Specification-Driven Development (SDD). • Experience with multi-agent frameworks and orchestration patterns. • Experience with MCP (Model Context Protocol) and Exposure to Agent-to-Agent (A2A) communication and agent interoperability concepts. • Experience with AI/LLM observability and evaluation platforms. • Knowledge of Redis, RabbitMQ, or similar technologies. • Knowledge of responsible AI, AI security, data privacy, prompt injection, and guardrail mechanisms. • Familiarity with cloud platforms such as Azure, AWS, or GCP.