Application Engineer II

About Us HighLevel is an AI powered, all-in-one white-label sales & marketing platform that empowers agencies, entrepreneurs, and businesses to elevate their digital presence and drive growth. We are proud to support a global and growing community of over 2 million businesses, comprised of agencies, consultants, and businesses of all sizes and industries. HighLevel empowers users  with all the tools needed to capture, nurture, and close new leads into repeat customers. As of mid 2025, HighLevel processes over 4 billion API hits and handles more than 2.5 billion message events every day. Our platform manages over 470 terabytes of data distributed across five databases, operates with a network of over 250 microservices, and supports over 1 million hostnames. Our People With over 1,500 team members across 15+ countries, we operate in a global, remote-first environment. We are building more than software; we are building a global community rooted in creativity, collaboration, and impact. We take pride in cultivating a culture where innovation thrives, ideas are celebrated, and people come first, no matter where they call home. Our Impact As of mid 2025, our platform powers over 1.5 billion messages, helps generate over 200 million leads, and facilitates over 20 million conversations for the more than 2 million businesses we serve each month. Behind those numbers are real people growing their companies, connecting with customers, and making their mark - and we get to help make that happen. Who You Are: You are an AI-focused software engineer with strong backend fundamentals and hands-on experience delivering LLM-enabled applications. You have delivered production systems and apply a reliability-first approach across agentic orchestration and applied AI pipelines. You operate with increasing autonomy on well-scoped problems, while collaborating closely with senior engineers on system design, architecture decisions, and long-term technical direction. You have hands-on experience implementing LangGraph workflows in production environments. You can take a workflow from vague intent to a shippable service with clear contracts, guardrails, evaluation, and observability. You operate effectively in cross-functional delivery environments. You can partner directly with Business Analysts and org leaders to clarify outcomes, translate business needs into acceptance criteria, and communicate tradeoffs without getting stuck in ambiguity. You bring delivery discipline, iterate efficiently, and focus on measurable outcomes. You apply a production engineering mindset. You build AI systems that are adopted in day-to-day operations, with monitoring, cost controls, and reliability built into the implementation. What You’ll Be Doing: - Design, implement, and iterate on LangGraph workflows that support structured requirement capture, clarification loops, stateful routing, and tool orchestration. - Build reusable workflow components (nodes, validators, schema-enforced outputs, guardrails) and standard patterns for retries, fallbacks, and human-in-the-loop review where appropriate. - Implement robust tool execution patterns (timeouts, error taxonomy, idempotency where needed) and ensure safe, deterministic structured outputs for downstream automation. - Partner with Business Analysts and org leaders during discovery and refinement to clarify goals, constraints, and success metrics; translate requirements into sprint-ready tickets and acceptance criteria. - Build and maintain agent-callable services/APIs for Snowflake retrieval, operational actions (e.g., Slack/workflows), and data enrichment/packaging. - Establish evaluation and observability for agent workflows (golden cases, regression suites, tool mocks; latency/cost/error monitoring) and contribute to pragmatic SLOs. - Contribute to our in-house transcription application through agent adjustments and infrastructure improvements, including transcript ingestion, normalization (timestamps/speaker turns/metadata), storage, and retrieval surfaces for downstream tooling. - Improve reliability of transcription pipelines through queue/worker architectures, retries/backoff, idempotency, and replay tooling. - Implement and iterate on rubric-based call scoring (versioned rubrics, explainable outputs, calibration support) and publish scoring artifacts that are usable for coaching and operational workflows. - Implement streaming considerations where applicable (session handling, backpressure/reconnect patterns) and ensure clean handoffs between capture, transcription, and scoring stages. - Deliver scoped components and workflows with guidance on architecture and design decisions from senior engineers or tech leads. What You’ll Bring: - 3–6 years of software engineering experience (backend/services preferred). - LangGraph experience is required (demonstrated implementation of real workflows).Strong proficiency in Python and/or TypeScript, including API/service design. - Experience building production services in a compiled, runtime-stable language (e.g., Go) for high-throughput, low-latency workloads (streaming I/O, async workers, and media/transcription pipelines). - Strong SQL skills; comfort working with warehouse-backed applications (Snowflake preferred). - Practical experience shipping LLM applications: - tool/function calling, structured outputs, and schema validation - retrieval patterns (RAG), routing, and context management - prompt/version management and evaluation methodologies - Production engineering fundamentals: - retries/idempotency, async job patterns, monitoring, and cost controls - Strong communication and stakeholder partnership skills: - ability to lead technical discovery conversations - ability to translate business intent into acceptance criteria and implementation plans - Botpress or similar agent builders (agent authoring and tool packaging). - Streaming systems exposure (WebSockets / HTTP2), backpressure + reconnect logic. - Audio fundamentals (FFmpeg basics, codecs) and experience with transcription providers. - Serverless/event-driven architectures (Cloudflare Workers/AWS), queues, observability platforms. - Experience contributing to production systems and growing ownership over time, with mentorship from senior engineers.