Modernization Engineer/FDE

Description

The company runs and redesigns critical technology systems for large international businesses, combining proven expertise with a steady stream of AI-based insight to support smarter decisions and faster innovation.

You would join as a Forward Deployed Engineer, working directly with strategic customers on AI adoption and modernization projects.

The company describes a flexible, supportive setting that pays attention to employee well-being and gives people room to grow.

Responsibilities

• Own customer delivery from scoping and prototyping through implementation, production rollout, adoption and optimization.
• Build generative AI, agentic AI, multi-agent and RAG-based solutions that create business value in complex enterprise environments.
• Act as a trusted technical partner, linking hands-on engineering work to customer results.
• Combine engineering, consulting and product knowledge to shape practical solutions, focusing on AI adoption and modernization.
• Turn vague requirements into structured designs, prototypes, implementation plans and adoption paths.
• Learn how customers work, including their challenges, operating limits, current workflows and goals.
• Lead working sessions with customer stakeholders and with product, engineering and consulting teams.
• Record field lessons, blockers, best practices and improvement ideas for reusable platforms, playbooks and delivery assets.
• Design and iterate custom AI and modernization systems using current software engineering methods.
• Build agent workflows, RAG solutions, API integrations, enterprise automation patterns and production-ready prototypes.
• Check performance, scalability, reliability, security and guardrails before and after go-live.
• Document architecture decisions, dependencies, runbooks, design choices and reusable patterns.
• Connect AI systems to enterprise APIs, cloud platforms, data stores, knowledge sources, vector databases and operational workflows.
• Contribute code, patterns, feedback and field insight to platforms, frameworks, agent catalogs and accelerators.
• Keep up with emerging AI, cloud and engineering tools and apply them to proposals, prototypes and engagements.

Requirements

• Hands-on development in Python, or in languages such as C, Node.js or TypeScript.
• Solid software engineering basics: SDLC, Git/GitHub, APIs, backend services, automated testing, CI/CD and secure coding.
• Experience designing, building or integrating AI solutions with frameworks or tools such as LangChain, LangGraph, Semantic Kernel, Microsoft Agent Framework, AutoGen, CrewAI or equivalent.
• Understanding of AI/ML frameworks and open-source AI tools such as TensorFlow, PyTorch, Hugging Face or comparable technologies.
• Knowledge of RAG architectures, prompt engineering, structured and unstructured knowledge sources, SQL and NoSQL databases and vector databases.
• Experience with cloud platforms (Azure, AWS or GCP), containers and distributed systems, including Docker and Kubernetes.
• Ability to check and improve the performance, scalability, reliability, observability, data protection and security of AI solutions.
• Strong consulting and stakeholder skills: framing business problems, communicating with executives and turning requirements into technical outcomes.
• T-shaped profile with broad knowledge of AI/ML, cloud, data, consulting and delivery, and deep expertise in one or two areas.
• Comfortable working in ambiguous, fast-moving settings, with ownership, curiosity and a drive to keep learning.
• Preferred: degree in Computer Science, Informatics, Data Science, Engineering or a related field, or equivalent practical experience.
• Preferred: real project experience implementing, operating and improving AI, Generative AI, Agentic AI or multi-agent solutions in production.
• Preferred: experience building full-stack prototypes or production systems across frontend, backend, API, data and cloud services.
• Preferred: experience with agent telemetry, observability, evaluation frameworks, guardrails, policy-as-code, knowledge graphs or semantic knowledge layers.
• Preferred: experience integrating AI into enterprise systems such as APIs, workflows, data platforms, knowledge repositories and cloud services.
• Preferred: experience working with customers in regulated or mission-critical environments where reliability, security and compliance are essential.
• Preferred: experience contributing reusable assets, reference implementations, solution patterns, code libraries, delivery playbooks or platform improvements.

Conditions

Hybrid-friendly work culture.

The company welcomes people of all cultures, backgrounds and experiences, and encourages you to apply even if you do not meet every requirement.

Benefits

• Be Well programs supporting financial, mental, physical and social health.
• Tools to plan your career path, personal development goals aligned with your ambitions, and regular feedback.
• Learning opportunities, including certifications with Microsoft, Google and Amazon, coaching and hands-on experience.