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 roadma
