Live Jobs

Our client is a growing software engineering group fostering a culture rooted in strong engineering practices, ownership, and transparency. The organization operates at the intersection of technology, implementation, and client collaboration, working across the full software development lifecycle (SDLC) with clients and engineering teams alike.

We are looking for an AI/ML Platform Architect to take ownership of the AI strategy, architecture, and technical evolution of the company’s core platform.

This is a highly hands-on role. You will not simply create architecture diagrams or oversee delivery from a distance — you’ll define the strategy, make technical decisions, and write production code alongside the engineering team.

You’ll work closely with Product, Engineering, Customer Success, partners, and the company’s Customer Advisory Board to translate real customer problems into scalable AI capabilities.

The company is open to two types of profile:

• An established AI/ML Architect who can immediately take ownership of the platform.
• A highly accomplished Senior / Staff-level AI Engineer who has the technical depth, product mindset, and ambition to grow into the architecture role.

Initially, this is an individual contributor position with significant strategic influence. As the engineering team expands, the person can either move toward technical management and build a team underneath them or remain a senior individual contributor at the top of the technical organization.

We are looking for a Senior/Staff Kotlin Engineer to join a small, flexible team building advanced developer tooling. This is a technology-focused engagement where the end-users are developers themselves, working on IntelliJ plugins, IDEs, coding agents, and local AI inference frameworks.

This is a senior/staff-level role offering high operational autonomy. The engineer will be expected to own features end-to-end, from design and scoping through to testing and implementation, with minimal oversight. No specific reporting line or time zone overlap requirement was specified beyond a remote-first working setup.

The client is an innovative technology company focused on AI-engineered software products. They operate within a disciplined, metrics-driven engineering environment that prioritizes predictable delivery, strong technical governance, and high execution quality. Their ecosystem utilizes modern AI-augmented delivery models,such as agentic coding workflows to drive product success and scale software solutions reliably.

This position is responsible for designing, building, and maintaining the AI systems: the LangGraph agents, retrieval-augmented generation (RAG), multi-provider LLM orchestration, and prompt/evaluation machinery that turn customer conversations and uploaded documents into grounded, auditable cost estimates. Good leadership skills and the ability to work in US time zones as required are both prerequisites.

Our client is a European software engineering and consulting company with a dedicated core team specializing in Bazel. The team delivers consultancy, migrations, and open-source contributions around Bazel, working across multiple ecosystems and codebases of varying size and complexity, from startups to world-class builds at large-scale organizations. The engineering culture is elite and specialized, focused on solving build-system problems too complex for client teams to resolve on their own, even with LLM assistance.

We are looking for a Bazel Engineer (Senior) to join a core Bazel team delivering a mix of consultancy, migration work, and open-source contributions. Team members work on short- to medium-length projects around Bazel, with the potential to become founding members of longer-lived teams over time.
This is a senior-level, highly autonomous role within an elite engineering squad of 10 Bazel experts working across various short and mid-term projects.

As an ML Engineer, you will focus on enabling the rapid exploration, productionization, and deployment of machine learning models and optimization algorithms. The goal is to deliver end-to-end ML lifecycles and robust APIs that solve complex pricing class problems across multiple business domains within a hybrid-cloud ecosystem.

Maintain, scale, and optimize core on-premise infrastructure serving over 10,000 developers. The primary focus is driving the reliability and efficiency of essential developer tools and services (such as Jenkins, Bitbucket, and BuildBarn) through solid system administration, networking, and automation across cross-ecosystem environments.