Qentelli - Lead AI Engineer - Conversational & Generative AI

Job Summary: We are seeking a Lead AI Engineer to drive the design, development, and deployment of our conversational AI and generative AI systems, including LLM-powered chatbots, Retrieval-Augmented Generation (RAG) pipelines, and agentic AI applications. This is a hands-on technical leadership role - you'll architect production-grade AI systems, guide a team of AI/ML engineers, and work closely with Product and Data Engineering to deliver reliable, scalable, and safe AI experiences. Key Responsibilities: - Architect and lead development of LLM-based applications, including chatbots, virtual assistants, and copilots. - Design and implement RAG pipelines - including chunking strategies, embedding generation, vector search, re-ranking, and prompt construction. - Build and maintain agentic workflows using frameworks such as LangChain, LlamaIndex, or custom orchestration layers. - Design prompt engineering and prompt management systems, including versioning and A/B testing of prompts. - Implement evaluation frameworks for LLM output quality - hallucination detection, relevance scoring, latency, and safety benchmarks. - Build robust MLOps/LLMOps pipelines for model deployment, monitoring, versioning, and rollback (CI/CD for AI systems). - Ensure systems are designed for low latency, scalability, and cost-efficiency in production environments. - Collaborate with Data Engineering to ensure clean, structured data feeds into embeddings and knowledge bases. - Implement guardrails, content moderation, and safety mechanisms to mitigate prompt injection, data leakage, and harmful outputs. - Mentor and provide technical leadership to a team of AI/ML engineers; conduct design and code reviews. - Stay current with the fast-evolving GenAI/LLM landscape and evaluate new tools, models, and techniques for adoption. Required Skills & Qualifications: - 6+ years of experience in AI/ML engineering, with 2+ years in a lead or senior technical capacity. - Strong programming skills in Python, with production experience in ML/AI systems. - Hands-on experience building LLM applications: chatbots, RAG systems, or generative AI products in production. - Practical experience with RAG components: chunking strategies, embedding models, vector databases, retrieval and re-ranking. - Experience with LLM orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, or similar. - Experience working with LLM APIs and platforms (OpenAI, Anthropic Claude, Google Gemini) and/or hosting open-source LLMs (Llama, Mistral, Falcon). - Familiarity with fine-tuning techniques (LoRA, QLoRA, PEFT, RLHF) and when to apply them vs. prompting/RAG. - Experience with vector databases and semantic search (Pinecone, Weaviate, Milvus, FAISS, pgvector). - Solid understanding of MLOps/LLMOps practices: model versioning, monitoring, A/B testing, CI/CD for ML. - Experience with cloud AI platforms (AWS Bedrock/SageMaker, GCP Vertex AI, Azure OpenAI). - Strong grasp of evaluation methodologies for generative AI (hallucination rate, groundedness, relevance, latency/cost trade-offs). - Understanding of AI safety and responsible AI practices - guardrails, bias mitigation, prompt injection defense. - Experience mentoring engineers, driving architecture decisions, and leading technical roadmaps.