AI Agentic Engineer/ Forward Deployment Engineer

Your next challenge

Bring your AI engineering expertise to a role where you can turn complex commercial challenges into practical, scalable solutions. As an AI Agentic Engineer/ AI Forward Deployment Engineer, you’ll design and deploy cloud-native AI systems that help global business teams improve pricing and channel management. Working in a hybrid model, you’ll collaborate with product and business stakeholders, connect technical decisions to commercial outcomes, and see your solutions through from design to deployment.

What you’ll do

· Take ownership of AI-enabled solutions from design through deployment, using AWS Bedrock and major cloud platforms to tackle pricing and channel management challenges for global business units.

· Design secure, scalable environments across AWS, GCP, and Azure, supporting high-availability AI workflows and data pipelines for revenue optimization.

· Build reliable PHP backend services that connect cloud APIs and internal systems, bringing pricing rules and channel partner programs into operation.

· Use Python automation to connect data ingestion, model execution, and reporting-helping pricing teams spend less time on manual tasks and make decisions faster.

· Turn machine learning and generative AI capabilities into working Python applications, with the monitoring, logging, and error handling needed for consistent performance across hybrid environments.

· Work alongside pricing analysts and channel managers to translate their needs into solutions for margin management, discount structures, and partner incentives.

· Shape the dataflows behind pricing and channel management, creating clean data models, efficient storage, and timely access for analytics and day-to-day operations.

· Help establish coding standards, configuration management, and deployment practices that make AI and automation solutions easier to maintain, observe, and operate reliably.

· Tune cloud applications and automation workflows to meet latency, throughput, and cost-efficiency targets linked to commercial objectives.

· Partner with product, finance, and sales operations to validate outcomes and refine AI models using market feedback.

· Make your work reproducible by creating clear solution architectures, operational runbooks, and technical decision records that others can build on.

· Share your expertise with junior engineers through design reviews, pair programming, and guidance on cloud-native AI patterns.

· Build security, compliance, and governance into your cloud and automation work, reducing operational risk while supporting strategic pricing initiatives.

What you’ll bring

· Extensive experience designing and deploying AWS solutions, including hands-on use of AWS Bedrock and broader AWS services for production AI workloads.

· Strong practical expertise in at least one additional major cloud platform-GCP or Azure-with experience in networking, storage, identity, and managed data services.

· Solid PHP development skills, with the ability to build reliable REST-based services, integrations, and business logic for pricing and channel management workflows.

· Advanced Python and automation skills, including scripting libraries, testing frameworks, and orchestration tools for large-scale data and AI operations.

· Deep knowledge of pricing and channel management, including discount models, deal structures, partner tiers, and performance measurement.

· The ability to collaborate effectively in a hybrid, day-shift role, combining remote teamwork with onsite engagement to keep delivery moving.

What you can learn and develop

· Deepen your hands-on experience with AWS Bedrock and production AI workloads, while broadening your cloud engineering practice across AWS, GCP, and Azure.

· Develop your ability to take machine learning and generative AI applications into operation, with a focus on monitoring, reliability, security, and governance.

· Build a stronger understanding of how pricing strategies, discount structures, partner incentives, and channel programs influence commercial outcomes.

· Sharpen your automation and data engineering skills by connecting data ingestion, model execution, and reporting in Python-based workflows.

· Strengthen your stakeholder collaboration and technical leadership through cross-functional delivery, design reviews, pair programming, and mentoring junior engineers.