Senior Software Engineer, AI

About the role

You will partner with Operations to understand workflows and translate process knowledge into agent capabilities, building AI agents that transform operations at scale through an agent platform. This role designs and owns core platform components, from orchestration to tool integrations, balancing agent design, backend architecture, and knowledge infrastructure.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Knowledge and memory infrastructure is architected so agents can retrieve data and act across our systems, enabling operation across environments while maintaining context. Core components of the agent platform are designed, built, and owned from orchestration through tool integrations to internal systems, replacing bespoke workflows with reasoning-driven operations. Engineers on the growing team are mentored as product complexity increases, supporting expanded ownership.

Requirements

Five or more years of backend experience building and operating production systems are required, demonstrated through shipped systems. Hands-on experience with LLMs and AI agents, including orchestration, RAG, or function calling, is required. Strong API design and system integration skills are required for reliable agent communication. A product-minded approach is applied to question requirements and contribute to product decisions. Ownership of complex projects in ambiguous, early-stage environments is proven. Production systems are confidently designed, built, and operated using modern backend technologies.

Nice to have

Experience with Golang, Ruby on Rails, JavaScript, Java, C#, Python, VueJS, MySQL, Kafka, AWS, Git, Memcache, Redis, ElasticSearch, Docker, Terraform, Kubernetes, CircleCI, and DataDog is considered.

Practical notes

This role operates from a Remote

International location with open visa sponsorship.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

AI agent platforms rely on orchestration, reasoning, and integration layers to replace bespoke workflows with reusable knowledge. Backend engineering in agent systems emphasizes reliability, observability, and safety controls for production use. Rapid experimentation and iterative delivery are common in early-stage, product-driven AI teams. Cross-functional collaboration with operations teams ensures agent capabilities align with real-world workflows.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.