Senior Software Engineer II

LVT•Seattle, WA

About The Position

LVT is building an agentic AI platform that enables autonomous agents that perceive real-world environments, reason over them, and act. Agents operate on an event pipeline that feeds them what's happening across tens of thousands of edge devices and carries their decisions back out to the field. This role builds and hardens the cloud backbone of that platform - the event pipeline agents consume from and act through, and the context stores they query. You'll own the cloud-side reliability, throughput, and scale of the event pipeline, the router/dispatcher that fans events to intelligence services, the projections that make events queryable (including the agent-facing, MCP-exposed context layer), and the observability that keeps it healthy under load. You will build the platform that lets agents run reliably at fleet scale. You'll partner closely with the edge team that produces events, the data team that owns the lakehouse you feed, infrastructure/operations who run the managed log and broker, and the engineers building the agent and CV workflows that consume your platform.

Requirements

6+ years building and operating backend or distributed systems in production at scale, with strong systems and API design experience.
Hands-on with Kafka or a comparable log/streaming system; topic and partition design, consumer-group semantics, delivery guarantees, replay, and schema-registry integration.
A track record of hardening services for production including SLOs, metrics and alerting, dead-letter and failure handling, and the operational maturity to own on-call trade-offs.
Comfortable choosing and operating different stores per access pattern (document/relational hot stores; object storage / lakehouse for cold and analytical).
Strong in Go and/or Python; experience building APIs and large-scale backend services.
AWS and Kubernetes, with a bias toward open, cloud-portable components.
Bachelor's or Master's in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Building agent-facing or MCP data-access layers, governed query interfaces over operational data for autonomous consumers.
Integrating agent or CV runtimes onto an event pipeline 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).

Responsibilities

Harden the event pipeline: Make the cloud pipeline production-grade consumer reliability, delivery and ordering guarantees, replay, idempotency, dead-letter handling, and schema/contract enforcement at the boundary.
Serve the agent layer: Make the dispatch path to agent and CV workflows reliable, and expose the event projections via MCP / agent-facing query interfaces that agents read for context, so the agentic platform has a dependable substrate to run on.
Scale ingress and projections: Stand up and scale the materialized stores the pipeline serves from (operational hot store, cold capture into the lakehouse), keeping each projection fit to its access pattern and rebuildable from the log.
Observability and operability: Own the signals that make the pipeline supportable, own dashboards, alerting, and feature flags.