Forward Deployed Engineer

What you will do

Define & Champion the AI Development Life Cycle: Partner with engineering and platform teams to establish a repeatable AI-SDLC framework - from prompt engineering and code generation through review, testing, and production deployment. Identify where governance, quality gates, and security checks must be embedded to keep AI-assisted development trustworthy at scale.

Sonar Integration & Enablement: Lead hands-on integration of Sonar products into customer CI/CD pipelines and AI-assisted workflows. With internal R&D in the loop, design implementations you will own end-to-end - integrating, optimizing, and productionizing within the client's existing ecosystem. Configure rules, quality profiles, and security policies aligned to each customer's risk tolerance and development maturity.

Code Quality & Security Guidance: Advise customers on eliminating technical debt and vulnerabilities surfaced by AI-generated code. Translate Sonar findings into developer-friendly workflows that catch issues early - before they reach production - without slowing delivery velocity.

AI Token Cost Management: Help customers instrument and optimize their AI toolchain spend. Define context strategies, caching patterns, and quality gates that reduce redundant generation cycles, keeping token consumption predictable and tied to business value.

Customer Modernization Advisory: Assess current development practices and roadmap a phased transition to AI-native workflows. Deliver workshops, architecture reviews, and executive briefings that build organizational buy-in and accelerate time-to-value.

Build Reusable Deployment Playbooks: Define and execute a technical strategy for integrating AI-SDLC solutions across diverse client environments — accounting for sector-specific security standards and performance SLAs. Package learnings into reusable playbooks and libraries that accelerate future deployments.

Build Strategic Client Relationships: Operate with autonomy and agency to create deep technical partnerships with client engineering, data science, MLOps, and infrastructure teams. Drive high-value, referenceable production deployments that become the foundation for broader adoption.

Serve as the Internal Voice of the Customer: Act as the primary internal consultant - advising product, research, and sales on real-world infrastructure limitations, performance bottlenecks, and emerging technical standards that shape product direction.

Experience and qualifications

Education: Bachelor's degree in Computer Science or a related field.

Experience: 2+ years in a technical, customer-facing role — Forward Deployed Engineer, Solutions Engineer, or Software/ML Engineer with consulting experience.

AI-SDLC Expertise: Hands-on experience with CI/CD pipelines and AI development workflows, with a track record of delivering complex production environments.

Forward Deployed / Consulting Background: Proven success working within or alongside client engineering teams to deploy and integrate high-performance AI systems — cloud or on-premise.

Technical Depth: Fluency in modern AI coding models, Python, and deployment technologies including Docker, Kubernetes, and cloud AI platforms (SageMaker, Vertex AI, Azure AI).

Communication: Ability to explain complex model performance and system architecture to both engineers and executives — translating technical detail into clear business value.

Subject Matter Expertise: Continuously current on LLM capabilities and implementation patterns; self-driven to learn and teach.