Are you looking to thrive in a stimulating work environment?

Join Levio, a leader in digital transformation, and take your career to the next level. You will work alongside high-caliber professionals on ambitious, large-scale technology projects, directly embedded in our clients’ environments. At Levio, we value expertise, curiosity, and continuous improvement — and we give you the space to grow.

About the Role

We embed engineers directly inside enterprise clients to find, build, and ship AI and agentic systems that reach production and stay there. A Forward Deployed Engineer - FDE runs discovery with the customer's own stakeholders, scopes the use cases worth building, then writes and deploys the working system — in the customer's environment, against their data, under their security and governance constraints.

This is a build-with-the-customer role, not an advisory one. You are the person in the codebase and in the room, closing the gap between an ambiguous "we should use AI" and a governed system operators actually trust and use. You own outcomes through real production usage — you do not hand off a slide deck or a prototype and move on.

Why Join Levio?

Work on complex, high impact digital transformation projects
Collaborate with experienced, multidisciplinary teams
Continuously develop your technical and professional expertise
Enjoy flexibility, autonomy, and a strong people first culture
Be part of an organization that values diversity, inclusion, and innovation

Role and Responsibilities

Run discovery with the customer's business stakeholders and turn open-ended "we should use AI" asks into scoped use cases, ranked by ROI, data readiness, and technical feasibility.
Build and ship production GenAI and agentic systems end to end — retrieval (RAG), agent orchestration and tool/MCP integration, evaluation harnesses, and the surrounding application and data plumbing.
Deploy inside the customer's environment and constraints: their cloud, their security posture, their data governance — not a sandbox you control.
Design for trust — keep business-critical logic (pricing, rules, calculations) in deterministic code paths, build evaluation and human-in-the-loop review, and know when model output is safe to put in front of operators.
Embed and enable: document the system, run walkthroughs, and lift adoption — while owning the system through real production use, not until the demo.
Present architecture, cost, and delivery trade-offs directly to non-technical decision-makers and keep them bought in.

Qualifications and Experience

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field
6+ years of experience in engineering roles, with 3+ years in GenAI/Agentic Systems positions
Strong communication skills and the ability to explain technical concepts and trade-offs to both technical and non-technical audiences.
Proven ownership of systems through production deployment, adoption, and iteration.

What we require

You ship code now. Recent, hands-on, first-person production work you can walk through line by line — not architecture you directed or reviewed. If your last real commit was two years ago, this isn't the role.
Real engineering fundamentals. You can drop into an unfamiliar codebase, debug code you didn't write, and build the thing when the platform's GUI can't express it and the AI assistant is stuck. Low-code and AI-assisted development are welcome as tools, not as the whole toolbox.
Genuine customer-facing experience. You've been embedded with customers — in their environment, their mess, their competing priorities — and navigated ambiguity and stakeholders with no direct authority. Internal-only product work doesn't demonstrate this.
Demonstrable production GenAI depth. You've built real RAG and/or agentic systems and can explain the mechanics — retrieval strategy, orchestration, tool-calling — and, above all, how you evaluated them and made them safe to trust. A buzzword list is not depth.
Evaluation and guardrail instinct. You keep the stochastic model out of business-critical decisions, and you can prove your AI output is trustworthy rather than hoping it is.