Senior AI Engineer

We are looking for a passionate Senior AI Engineer to design, develop, and deploy Generative AI solutions using Large Language Models (LLMs), Retrieval Augmented Generation (RAG) pipelines, and modern AI frameworks like LangChain, LlamaIndex, and Hugging Face. You'll work on building intelligent, production-ready systems that enhance how users search, generate, and interact with information.

Responsibilities:

• Design and implement end-to-end Generative AI systems using LLMs and RAG architectures.
• Build scalable retrieval and vector search pipelines using tools like Pinecone, FAISS, ChromaDB, or Milvus.
• Develop custom agents, chains, and workflows using LangChain, LlamaIndex, or similar orchestration frameworks.
• Integrate OpenAI, Anthropic, Llama, or Mistral models via APIs or fine-tuning workflows.
• Optimise prompt engineering, context retrieval, and embedding strategies for real-world use cases.
• Collaborate with backend teams to deploy AI microservices via FastAPI, Flask, or similar frameworks.
• Implement MLOps practices for monitoring, versioning, and continuous delivery of AI models.
• Stay current with new developments in the LLM and GenAI ecosystem and recommend adoption strategies.

Requirements:

• Strong programming skills in Python.
• Hands-on experience with LangChain, LlamaIndex, or OpenAI / Hugging Face ecosystems.
• Experience building RAG pipelines and integrating vector databases (e. g., Pinecone, FAISS, ChromaDB).
• Deep understanding of LLM architectures, tokenisation, and prompt optimisation.
• Experience with PyTorch or TensorFlow for model training or fine-tuning.
• Familiarity with Docker, Kubernetes, and cloud deployment (AWS / GCP / Azure).
• Strong grasp of data engineering workflows, API integration, and system design.

Good to Have:

• Experience with LLMOps tools (e. g., Weights and Biases, MLflow, LangFuse, PromptLayer).
• Exposure to open-source LLMs (Llama 3 Mistral, Falcon, etc. ) and custom fine-tuning.
• Knowledge of knowledge graph integration or context-aware retrieval.
• Familiarity with frontend integration using Streamlit, Gradio, or similar frameworks.
• Contributions to open-source AI projects, research papers, or public demos.

Skills

FAISS, LLM, Pinecone, Python, RAG, large language model, retrieval augmented generation, vector database