Asks for

The ad says“Apply core data science methods — modeling, A/B testing, scoring, clustering, and time-series forecasting — to enhance agent intelligence and broader product features”
The ad says“Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools”
The ad says“Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting”
The ad says“Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript”
The ad says“Apply core data science methods — modeling, A/B testing, scoring, clustering, and time-series forecasting — to enhance agent intelligence and broader product features”
The ad says“Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques”
The ad says“Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques”
The ad says“Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e.g., FAISS, Pinecone, Weaviate)”
The ad says“Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques”
The ad says“Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques”
The ad says“Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools”
The ad says“Experience with LangChain or similar agent orchestration frameworks; ability to design multi-step, tool-augmented agents”
The ad says“Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques”
The ad says“Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools”
The ad says“Proven experience taking ML/AI solutions from prototype to production, including monitoring, observability, and model iteration”
The ad says“Proven experience taking ML/AI solutions from prototype to production, including monitoring, observability, and model iteration”
The ad says“Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e.g., FAISS, Pinecone, Weaviate)”
The ad says“Hands-on expertise with LLMs: fine-tuning, prompt engineering, function-calling agents, embeddings, and evaluation techniques”
The ad says“Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript”
The ad says“Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools”
The ad says“Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e.g., FAISS, Pinecone, Weaviate)”
The ad says“Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting”
The ad says“Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting”
The ad says“Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting”
The ad says“Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript”
The ad says“Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting”
The ad says“Experience working in cloud-native environments (GCP, AWS) and deploying models with frameworks like PyTorch, Transformers (HF), and MLOps tools”
The ad says“Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript”
The ad says“Solid foundation in core data science: supervised and unsupervised learning, causal inference, statistical testing, segmentation, and time-series forecasting”
The ad says“Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e.g., FAISS, Pinecone, Weaviate)”
The ad says“Proficiency in Python, with strong engineering practices (CI/CD, testing, versioning) and familiarity with TypeScript”
The ad says“Strong experience in building retrieval-augmented generation (RAG) systems using vector databases (e.g., FAISS, Pinecone, Weaviate)”

About HighLevel:
HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes.To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently.Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.

Our PeopleWith over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.

Our ImpactEvery month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. We’re proud to be a part of that.Learn more about us on our YouTube Channel or Blog Posts.

About the Role:

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.

Requirements:

8+ 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 skills—able to translate between technical constraints and product goals

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

EEO Statement:

The company is an Equal Opportunity Employer. As an employer subject to affirmative action regulations, we invite you to voluntarily provide the following demographic information. This information is used solely for compliance with government recordkeeping, reporting, and other legal requirements. Providing this information is voluntary and refusal to do so will not affect your application status. This data will be kept separate from your application and will not be used in the hiring decision.

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