Lead AI Engineer

We are looking for a visionary Lead AI Engineer to architect and implement the generative intelligence core of our upcoming project. This is not a traditional research role; we need a "Builder" who understands how to turn raw model capabilities into reliable, scalable, and cost-effective product features. As the Lead GenAI Engineer, you will design the RAG (Retrieval-Augmented Generation) architectures, select the appropriate model stacks, and ensure that our AI outputs are grounded, safe, and performant. You will work in lockstep with the Technical Leader to integrate AI services into the broader application ecosystem and mentor the team on AI engineering best practices. Requirements MUST 8+ years of professional experience in Software Engineering, with at least 2 years of focused experience building and deploying GenAI-powered applications. LLM Orchestration Mastery: Deep expertise in frameworks like LangChain, LlamaIndex, or Haystack for building complex chains and agents. RAG Architecture: Proven experience implementing Retrieval-Augmented Generation , including chunking strategies, embedding models, and vector database management (e.g., Pinecone, or pgvector ). Advanced Prompt Engineering: Expertise in systematic prompt optimization, few-shot prompting, and Chain-of-Thought techniques to minimize hallucinations. Model Integration & Selection: Deep understanding of the trade-offs between proprietary models (OpenAI, Anthropic, Gemini) and open-source models (Llama 3, Mistral) including hosting via Hugging Face or vLLM. Python Proficiency: Expert-level Python skills, including asynchronous programming and performance optimization for data-heavy workloads. Evaluation & Observability: Experience setting up AI evaluation frameworks (e.g., RAGAS, TruLens, or LangSmith ) to measure accuracy, latency, and cost. API & Backend Integration: Ability to design robust APIs (FastAPI/Flask) that handle the non-deterministic nature of LLMs, including streaming responses and graceful error handling. English C1: Ability to explain complex AI concepts (like temperature, top-p, or context windows) to stakeholders and non-technical clients. Nice to have Fine-tuning Experience: Practical experience fine-tuning open-source models (PEFT, LoRA, QLoRA) for specific domains or style-matching. LLMOps & Deployment: Experience with automated deployment of AI models using tools like BentoML, Modal, or AWS SageMaker. AI Security: Knowledge of LLM-specific vulnerabilities (Prompt Injection, data leakage) and mitigation strategies. Multi-modal AI: Experience working with Vision-Language models or Audio-to-Text/Text-to-Audio pipelines. Product Thinking: A strong sense of "AI UX"—understanding when a feature should be an agentic workflow versus a simple deterministic function. Highlights Originally posted on Himalayas