Senior Application Modernization Engineer

At Liatrio, we don't just ship software — we enable real transformation.

We help large enterprises break free from legacy systems and truly enable AI across their organizations. We design and deliver secure, scalable, agentic AI-native platforms that reshape how teams build, deliver, and thrive at scale.

We live this every day by embedding deeply with clients to drive meaningful AI enablement — accelerating modernization, reducing risk, and creating lasting competitive advantage through production-grade AI systems. We use tools like Claude, Cursor, and Windsurf daily to move faster and deliver better outcomes.

If you're a hands-on engineer who leads with AI and drives lasting change, this is where you belong.

We're looking for AI-enabled Application Modernization Engineers passionate about modernizing legacy software, building cloud-native and AI-augmented systems, and helping both client teams and our own engineers improve quality and speed of delivery. 

You will act as both a builder and an enabler — writing production-grade code and uplifting the engineers around you on modern AI-augmented ways of working.

You'll be embedded with enterprise clients building real software — modernizing systems that have been running the business for years and building new ones that need to run it for years to come. Day to day that looks like:

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  Breaking down monolithic legacy applications and rebuilding them as cloud-native services — designing APIs, decomposing tightly coupled systems, and migrating data without taking the business offline

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  Building full-stack, production-grade applications — frontend interfaces, backend services, REST and GraphQL APIs, and the integrations that tie them together in complex enterprise environments

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  Writing clean, testable, well-structured code across multiple languages and paradigms — and reviewing others' code with the same standard

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  Designing and implementing event-driven and microservices architectures that can scale, evolve, and be maintained by teams who didn't build them

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  Integrating AI and LLM capabilities directly into client applications — building agentic workflows, RAG pipelines, AI-augmented developer tooling, and intelligent automation that actually works in production

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  Setting up and improving CI/CD pipelines, test automation, and delivery infrastructure so teams can ship with confidence

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  Working alongside Operations teams to make sure what gets built can actually be run — containerized, observable, and operable in cloud environments

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  Mentoring and uplifting the engineers around you — pairing, code review, and real-time feedback that makes the whole team better

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  Keeping account leaders and architects informed of technical risk, blockers, and changes that affect the engagement

• You've built production applications from scratch and modernized legacy systems — comfortable with both greenfield and brownfield, and know when approaches like strangler fig or lift-and-refactor are appropriate

• You're fluent in multiple programming languages and paradigms — clean, testable code in both functional and object-oriented styles, knowing how to pick the right tool for the job

• You've delivered full-stack solutions — frontend web apps, backend APIs, cloud-hosted services, and standalone applications — and understand how those pieces fit together end-to-end in complex enterprise environments

• You've worked inside large enterprise environments with distributed systems and multi-cloud setups — you know how to navigate organizational complexity and deliver in spite of it

• You're comfortable owning your part of the SDLC end-to-end — writing code, reviewing PRs, writing tests, and empathizing with developers on either side of you

• You understand cloud-native architecture — containerization, microservices, event-driven patterns — and are comfortable with source control branching strategies and CI/CD automation

• You've either built or been closely embedded with Operations teams — you understand cloud-native platform adoption, whether that's Kubernetes, managed container services, or other orchestration platforms depending on client environment, infrastructure as code, observability, and what it means to build software that can actually be run in production

• You've built or integrated AI capabilities into real applications — you know what it takes to get an LLM-powered feature into production and keep it working

• You use AI coding tools like Claude, Cursor, or Windsurf as a natural part of how you work — as a way to move faster and produce higher quality output every day

• You have familiarity with retrieval-augmented generation, vector databases, or semantic search — either from building it or working alongside someone who has