High Level Services
Lead Software Engineer (AI - Data Science)
High Level Services, Dallas, Texas, United States, 75215
Overview
We're seeking a seasoned Engineer to lead the development of LLM-powered AI agents and next-gen Generative AI systems that drive core product functionality and customer-facing automation at scale. This is a high-autonomy, high-impact role for someone who thrives at the intersection of applied AI, agent design, and core data science. You'll build foundational models, retrieval systems, and dynamic agents that interact with millions of users, powering personalized communication, intelligent scheduling, smart replies, and much more. We are looking for builders who can take projects from research and experimentation to production and iteration, and who bring strong data science rigour alongside hands-on GenAI experience. Responsibilities Architect and deploy autonomous AI agents that execute workflows across sales, messaging, scheduling, and operations. Build and fine-tune LLMs (open-source and API-driven) tailored to HighLevel's unique data and customer use cases. Develop robust retrieval-augmented generation (RAG) systems and vector search infrastructure to enable context-rich, real-time generation. Design and iterate on prompt engineering, context construction, and agent tool usage strategies using frameworks like LangChain. Apply core data science methods, modeling, A/B testing, scoring, clustering, and time-series forecasting to enhance agent intelligence and broader product features. Partner with backend, infra, and product teams to build reusable, scalable GenAI infrastructure: model serving, prompt versioning, logging, evals, and feedback loops. Continuously evaluate and monitor agent performance, hallucination rates, and real-world effectiveness using rigorous experimentation frameworks. Influence HighLevel's AI roadmap while mentoring engineers and contributing to technical standards and best practices.
Requirements
5+ years of experience in Data Science, Machine Learning, or Applied AI, with a track record of delivering production-grade models and systems. Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques. Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e. g., FAISS, Pinecone, Weaviate). Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools. Experience with LangChain or similar agent orchestration frameworks; ability to design multi-step, tool-augmented agents. Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript. Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting. Proven experience taking ML/AI solutions from prototype to production, including monitoring, observability, and model iteration. Ability to work independently and collaboratively, leading initiatives and mentoring peers in a fast-paced, cross-functional environment. Strong product sense and communication skillsable to translate between technical constraints and product goals.
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We're seeking a seasoned Engineer to lead the development of LLM-powered AI agents and next-gen Generative AI systems that drive core product functionality and customer-facing automation at scale. This is a high-autonomy, high-impact role for someone who thrives at the intersection of applied AI, agent design, and core data science. You'll build foundational models, retrieval systems, and dynamic agents that interact with millions of users, powering personalized communication, intelligent scheduling, smart replies, and much more. We are looking for builders who can take projects from research and experimentation to production and iteration, and who bring strong data science rigour alongside hands-on GenAI experience. Responsibilities Architect and deploy autonomous AI agents that execute workflows across sales, messaging, scheduling, and operations. Build and fine-tune LLMs (open-source and API-driven) tailored to HighLevel's unique data and customer use cases. Develop robust retrieval-augmented generation (RAG) systems and vector search infrastructure to enable context-rich, real-time generation. Design and iterate on prompt engineering, context construction, and agent tool usage strategies using frameworks like LangChain. Apply core data science methods, modeling, A/B testing, scoring, clustering, and time-series forecasting to enhance agent intelligence and broader product features. Partner with backend, infra, and product teams to build reusable, scalable GenAI infrastructure: model serving, prompt versioning, logging, evals, and feedback loops. Continuously evaluate and monitor agent performance, hallucination rates, and real-world effectiveness using rigorous experimentation frameworks. Influence HighLevel's AI roadmap while mentoring engineers and contributing to technical standards and best practices.
Requirements
5+ years of experience in Data Science, Machine Learning, or Applied AI, with a track record of delivering production-grade models and systems. Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques. Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e. g., FAISS, Pinecone, Weaviate). Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools. Experience with LangChain or similar agent orchestration frameworks; ability to design multi-step, tool-augmented agents. Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript. Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting. Proven experience taking ML/AI solutions from prototype to production, including monitoring, observability, and model iteration. Ability to work independently and collaboratively, leading initiatives and mentoring peers in a fast-paced, cross-functional environment. Strong product sense and communication skillsable to translate between technical constraints and product goals.
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