Senior AI Engineer

Workato; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Workato is seeking a Senior AI infrastructure engineer to build the services that connect enterprise applications with large language model providers. You will own gateways, routing, authentication, and observability, from design through deployment and monitoring.

This role emphasizes high-performance, low-latency systems, PostgreSQL ownership, and deep experience with Go or Ruby, Kubernetes, and distributed systems.

Qualifications

• Minimum 5 years shipping and operating production services in Go or Ruby.
• Deep PostgreSQL knowledge: indexing, transactions, pooling, migrations.
• Experience with concurrency, Kubernetes, deployment pipelines and security.
• Experience with LLM protocol level integration, including messaging, tool calling, streaming, provider APIs and latency.

Responsibilities

• Design and develop MCP and AI gateways that mediate between applications, agents, and model providers.
• Implement protocol-level capabilities including MCP clients and servers, request routing, streaming, tool-call proxying, authentication, authorization, and tenant isolation.
• Build high-throughput, low-latency services involving TCP, TLS, HTTP/1.1, HTTP/2, JSON streaming, connection pooling, and backpressure.
• Own application-level PostgreSQL concerns such as schema design, indexing, query planning, transactions, connection management, and large-scale migrations.
• Design concurrent systems using worker pools, queues, graceful shutdown, and race-free shared state; profile services to identify and resolve bottlenecks.
• Establish useful observability for AI workloads, including metrics, tracing, logs, token usage, provider latency, cache performance, failure modes, and request cost.

Skills

Go

Ruby

PostgreSQL

Distributed systems

Kubernetes

Profiling

Tools

Kubernetes

Docker

CI/CD

Networking

Job description

Role overview

Senior AI infrastructure engineer building the services that connect enterprise applications and systems with large language model providers. The focus is on production-grade gateways and supporting services for routing, authentication, authorization, rate limiting, protocol translation, tenant isolation, streaming, and observability. This is a systems-oriented role for someone comfortable owning services from design and implementation through deployment, monitoring, and continuous improvement.

Responsibilities

• Design and develop MCP and AI gateways that mediate between applications, agents, and model providers.
• Implement protocol-level capabilities including MCP clients and servers, request routing, streaming, tool-call proxying, authentication, authorization, and tenant isolation.
• Build high-throughput, low-latency services involving TCP, TLS, HTTP/1.1, HTTP/2, JSON streaming, connection pooling, and backpressure.
• Own application-level PostgreSQL concerns such as schema design, indexing, query planning, transactions, connection management, and large-scale migrations.
• Design concurrent systems using worker pools, queues, graceful shutdown, and race-free shared state; profile services to identify and resolve bottlenecks.
• Establish useful observability for AI workloads, including metrics, tracing, logs, token usage, provider latency, cache performance, failure modes, and request cost.

Requirements

• At least five years of senior-level experience shipping and operating production services in Go, Ruby, or both.
• For Go-focused candidates, strong knowledge of the standard library, networking, TLS, concurrency, memory behavior, allocation patterns, garbage collection, and lock contention.
• For Ruby-focused candidates, substantial Rails production experience, including ActiveRecord tuning and performance under load.
• Deep application-side PostgreSQL knowledge covering optimization, indexing, transactions, pooling, and migrations.
• Practical experience with concurrency, profiling, Kubernetes, containers, deployment pipelines, security, and distributed-system troubleshooting.
• Experience integrating with LLMs at the protocol level, including message formats, tool calling, streaming, caching, provider APIs, token usage, latency, and cost.

Nice to have

• Experience building or integrating MCP servers or clients and understanding transport and capability negotiation.
• Ability to audit and verify model output, identify security issues or hallucinated interfaces, and explain the underlying technical cause.
• Interest in evaluating AI-assisted development workflows, automated code review, agent tooling, and internal developer systems.

Benefits and work setup

• Flexible, trust-oriented working culture with significant ownership and emphasis on sustainable productivity.
• Employees may use AI and LLM development tools according to their preferences, with access to a broad range of models and coding tools.
• The source describes a benefits offering intended to support employees both at work and outside it, without specifying individual benefits.