Lead Engineer - Workflows AI
Job Description
HighLevel is seeking a Lead Engineer to spearhead their Workflows AI initiatives, focusing on building and scaling AI-powered automation features. This role sits at the intersection of cutting-edge AI technology and practical business applications, working with a stack that includes Vue, Node.js, MongoDB, and various AI tools. The company processes over 15 billion API hits daily and manages 470+ terabytes of data across five databases.
The ideal candidate will lead the technical strategy for AI in workflows, from prompt engineering to fine-tuning and evaluation frameworks. You'll work with various LLM providers (OpenAI, Anthropic, Claude), implement RAG pipelines, and create agentic AI workflows using modern frameworks like LangChain and AutoGen. The role requires both technical expertise and leadership skills, as you'll be mentoring engineers and driving technical decisions.
HighLevel offers a truly global, remote-first environment with over 1,500 team members across 15+ countries. The platform serves over 2 million businesses, handling 1.5 billion messages and generating 200 million leads monthly. This role presents an opportunity to impact millions of businesses while working with state-of-the-art AI technology in a rapidly growing company.
The position combines technical leadership, AI expertise, and full-stack development skills, making it ideal for someone who wants to shape the future of AI-powered workflow automation while leading a team and making a real-world impact.
Responsibilities For Lead Engineer - Workflows AI
- Lead the architecture, development, and deployment of AI-powered workflow automation features
- Integrate and orchestrate multiple LLM providers with high reliability
- Build and optimize RAG pipelines embeddings, chunking, hybrid retrieval using vector databases
- Design and implement agentic AI workflows multi-step reasoning, tool usage, memory leveraging LangChain, LangGraph, AutoGen, or similar
- Apply prompt engineering best practices: few-shot, chain-of-thought, dynamic context, function/tool calling
- Execute fine-tuning/adaptation workflows LoRA, QLoRA, PEFT, and embedding-based customization
- Develop and manage EVAL frameworks hallucination detection, groundedness, BERTScore, BLEU, GPTScore to track and improve model quality
- Build robust, scalable Node.js APIs and Vue-based frontends, integrating seamlessly with our stack
- Work with MongoDB, Firestore, ElasticSearch, Redis to design efficient data flows for AI workloads
- Ensure observability, performance tuning, and cost optimization in GCP-based deployments
- Collaborate closely with product, design, and infra teams to deliver high-impact features on time
- Mentor engineers, set best practices, and drive technical decision-making in the AI workflows domain
Requirements For Lead Engineer - Workflows AI
- Leadership & Ownership: Proven experience leading projects and mentoring engineers
- Fullstack Engineering: Node.js backend, Vue frontend, API design & scaling
- LLM APIs: OpenAI, Anthropic, Bedrock, Claude, LLaMA, Mistral, LlamaIndex
- Prompt Engineering: few-shot, CoT, dynamic context, function/tool calling
- RAG Pipelines: embeddings, chunking, hybrid retrieval
- Vector DBs: Pinecone, Weaviate, FAISS, Qdrant
- Agentic AI: multi-step workflows, tool usage, memory (LangChain, LangGraph, AutoGen)
- Fine-Tuning: LoRA, QLoRA, PEFT, embedding-based adaptation
- EVALs: hallucination detection, groundedness, BERTScore, BLEU, GPTScore
- Data Layer: MongoDB, Firestore, ElasticSearch, Redis
- Cloud & Deployment: GCP (preferred), AWS, or Azure; Docker/Kubernetes; CI/CD for AI workloads
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