- A Generative AI (GenAI)

- Engineer builds and deploys production-grade applications using large language models and multimodal systems. Professionals bridge frontier AI capabilities with enterprise software by implementing RAG pipelines, agentic workflows, and cloud-based deployments

Key Responsibilities

- Model Integration: Connect software with foundation models like GPT, Claude, Gemini, and Llama via APIs.

- RAG Pipelines: Build Retrieval-Augmented Generation systems using vector databases.

- Agentic Frameworks: Develop autonomous multi-step AI agents via frameworks like LangChain or LlamaIndex.

- Fine-Tuning: Adapt and evaluate models for custom enterprise datasets.

- Optimization: Monitor inference latency, output accuracy, and token costs. [1, 2, 3, 4, 5]

Required Qualifications & Skills

- Programming: Advanced Python proficiency and experience with AI libraries (PyTorch, TensorFlow).

- Frameworks: Hands-on skill with LangChain, LlamaIndex, and embedding models.

- Cloud Platforms: Deployment experience on AWS, Azure, or Google Cloud.

- Background: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field

📌 GEN AI (London)
🏢 Dev-Test- India
📍 London