AI Engineer (GenAI / LLMs / RAG)

Role Overview: - We are seeking an AI Engineer to design and deploy generative AI solutions, including LLM-based applications and Retrieval-Augmented Generation (RAG) pipelines, to solve real business problems. Key Responsibilities: - Design and implement LLM-powered applications (chatbots, copilots, agents). - Build and optimize RAG pipelines (chunking, embeddings, retrieval, reranking). - Fine-tune and evaluate LLMs for domain-specific use cases. - Integrate AI solutions into production systems via APIs. - Monitor model performance, cost, and latency in production. Requirements: - 3+ years of experience in AI\/ML, with 1+ year focused on GenAI\/LLMs. - Strong understanding of prompt engineering and embedding-based retrieval. - Experience with vector databases and orchestration frameworks. - Familiarity with LLM evaluation and hallucination mitigation techniques. Technical Stack \/ Tools: - Python, LangChain, LlamaIndex. - OpenAI, Anthropic, or open-source LLMs (Llama, Mistral). - Vector databases (Pinecone, Weaviate, pgvector). - Docker, FastAPI, cloud AI services (Azure OpenAI, AWS Bedrock, GCP Vertex AI). Location: Remote - Anywhere Skills required for this job: Application Programming Interface (API)Artificial intelligence (AI)DockerGenerative artificial intelligence (Generative AI)LangChainLarge language model (LLM)LlamaIndexMLPrompt engineeringPythonRetrieval-augmented generation (RAG)Vector databases