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Salary: USD 185,000 - 230,000 / annual
LVT (LiveView Technologies) is building an agentic AI platform that enables autonomous agents to perceive real-world environments, reason over them, and act. The company has been recognized as one of the Financial Times' Fastest Growing Companies 2025 and #10 on the Inc. 5000 Rocky Mountain Regional list.
In this role, you will own the cloud-side reliability, throughput, and scale of the event pipeline—the backbone that feeds autonomous agents with real-world data from tens of thousands of edge devices and carries their decisions back to the field. You will build and harden the platform that lets agents run reliably at fleet scale.
Key responsibilities include:
• Harden the event pipeline for production-grade reliability: implement consumer reliability, delivery and ordering guarantees, replay capabilities, idempotency, dead-letter handling, and schema/contract enforcement at system boundaries.
• Serve the agent layer: make the dispatch path to agent and computer-vision workflows reliable, and expose event projections via MCP (Model Context Protocol) and agent-facing query interfaces so the agentic platform has a dependable substrate.
• Scale ingress and projections: stand up and scale materialized stores (operational hot stores, cold capture into the lakehouse), keeping each projection optimized for its access pattern and rebuildable from the log.
• Own observability and operability: build the signals, dashboards, alerting, and feature flags that make the pipeline supportable and maintainable.
You will partner closely with the edge team producing events, the data team owning the lakehouse, infrastructure/operations running managed logs and brokers, and engineers building agent and CV workflows.
REQUIREMENTS:
• 6+ years building and operating backend or distributed systems in production at scale, with strong systems and API design experience
• Hands-on experience with Kafka or comparable log/streaming systems: topic and partition design, consumer-group semantics, delivery guarantees, replay, and schema-registry integration
• Track record hardening services for production: SLOs, metrics and alerting, dead-letter and failure handling, operational maturity for on-call trade-offs
• Comfortable choosing and operating different storage systems per access pattern (document/relational hot stores; object storage/lakehouse for cold and analytical)
• Strong proficiency in Go and/or Python; experience building APIs and large-scale backend services
• AWS and Kubernetes experience, with bias toward open, cloud-portable components
• Bachelor's or Master's in Computer Science, Engineering, or related field, or equivalent practical experience
PREFERRED QUALIFICATIONS:
• Building agent-facing or MCP data-access layers with governed query interfaces for autonomous consumers
• Integrating agent or CV runtimes onto event pipelines as pluggable consumers
• Stream-processing frameworks (Benthos/Bento, Flink, or similar)
• Schema and contract tooling (protobuf, Avro, schema registry)
• Lakehouse producer experience (Iceberg/S3, CDC); MQTT/IoT ingest at scale (EMQX or comparable)
