Qentelli - Lead AI Engineer - LLM/RAG

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.