engineering
Posted Feb 9Application Engineer II
at HighLevel
IndiaOn-site
Responsibilities
- - 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.
- - Build and maintain agent-callable services/APIs for Snowflake retrieval, operational actions (e.g., Slack/workflows), and data enrichment/packaging.
- - 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.
Requirements
- About us HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale.
- Learn more about us on our YouTube Channel or Blog Posts 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.
- experience implementing LangGraph workflows in production environments.
- You build AI systems that are adopted in day-to-day operations, with monitoring, cost controls, and reliability built into the implementation.
- 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