Lead/Principal Transformation Developer (AI Code Modernization Platform)

Position summary

Bentley Systems is forming a new, dedicated AI Systems Modernization team to lead the company’s efforts in applying advanced AI techniques—such as multi‑stage, agentic workflows—to automatically modernize academic and legacy codebases into cloud‑native services and applications. This global team will work at the intersection of AI research and real‑world software engineering, collaborating closely with product teams to support, guide, and enable AI adoption across Bentley.

Because this team is launching this year, the Lead / Principal Engineer will have significant influence over its direction, technical standards, and operating model. As a new and strategically important domain for Bentley, the team will play a leadership role in defining best practices, evangelizing AI‑driven modernization approaches, and operationalizing them across the organization.

This role is well-suited for engineers who enjoy bleeding‑edge technology, deep technical discussions, and exposure to a wide variety of technology stacks. Bentley’s portfolio spans hundreds of products—including desktop, mobile, traditional server‑based systems, and cloud microservices—offering exceptional breadth and the opportunity to work with a diverse, global team of experts.

Why This Role Matters

• Foundational impact – This team is starting this year. You will help define its mission, methods, and standards.
• Real‑world AI – Apply AI to large, production systems with strict requirements—not experiments or demos.
• Technical breadth – Work across legacy languages, modern stacks, and cloud architectures.
• Leadership with depth – Influence strategy while remaining hands‑on with architecture and prototyping.

What You’ll Work On

AI Platform & Architecture

• Define the technical vision and roadmap for Bentley’s AI code modernization platform
• Design multi‑stage, agentic AI workflows for automated code analysis, transformation, and uplift
• Build and evolve LLM pipelines, including orchestration, evaluation, monitoring, and iteration

Quality & Validation

• Create rigorous testing and validation strategies to ensure correctness, performance, and numerical precision
• Establish quality gates and acceptance criteria for production use
• Evaluate behavior across languages and runtimes to ensure fidelity of converted systems

Technical Leadership & Enablement

• Lead architectural decisions and help teams converge quickly on effective solutions
• Balance tradeoffs between speed, cost, quality, and risk
• Help hire, mentor, and guide a small team of technical experts
• Enable adoption across Bentley through best practices, tooling, and education

What Success Looks Like

Within your first 6–12 months, you will:

• Deliver a scalable, repeatable AI‑based modernization platform used by multiple teams
• Successfully modernize representative legacy systems into cloud‑native architectures
• Define company‑wide standards for AI‑assisted code conversion
• Clearly establish where AI automation works—and where human expertise is essential

What We’re Looking For

Required Experience

• Principal‑level experience leading complex technical initiatives
• Strong problem‑solving skills and experience working with large or legacy codebases
• Hands‑on experience with AI‑assisted development or LLM pipelines
• Proficiency with cloud platforms and modern service architectures
• Working knowledge of legacy languages (e.g., C, Fortran) and modern languages (e.g., Rust, .NET, C++)
• Understanding of legacy and modern code patterns and how they translate across paradigms
• Familiarity with numerical precision considerations across runtimes
• Experience with code profiling, performance analysis, and optimization, particularly in performance‑sensitive or numerically intensive systems
• Understanding of High‑Performance Computing (HPC) and parallel execution concepts relevant to modernizing scientific or engineering workloads
• Familiarity with GPU or accelerator‑based computing and how execution and memory models impact modernization feasibility and validation
• Working knowledge of CUDA or similar GPU programming models, sufficient to assess transformation impact on correctness, precision, and performance
• Strong communication skills and ability to influence across teams

Nice to Have

• Scientific or engineering software experience (e.g., Finite Element Analysis)
• Compiler theory, AST‑level code analysis, or developer tooling
• Experience with Azure and/or GCP
• Containerization and orchestration (Docker, Kubernetes, Istio)
• Application and cloud security practices
• Equivalence verification or validation techniques