Generative AI Developer

• 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.