Senior Software Engineer, Applied AI and Customer Solutions

About this role

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Skills you'll use

What you'll do

• Scope customer environments directly with executive sponsors and IT/data owners — mapping systems, data models, and workflows to identify the real business problem, not just the stated one
• Rapidly prototype and demo working solutions in front of customers, iterating in real time to prove value fast
• Serve as the bridge between customer & core engineering team to harden validated prototypes into production deployments
• Design and implement multi-tenant, hybrid, or customer-controlled deployment architectures, as per customer's data residency, privacy, and IT-maturity requirements
• Build and own identity and access management, encryption, and secure cross-network connectivity (mTLS, VPC peering/PrivateLink, API gateways) for customer-embedded deployments
• Bring security, data-residency and compliance judgment into discovery conversations before a commercial commitment is made, not after
• Own CI/CD, observability, and production support for systems living inside customer environments
• Recognize repeatable patterns across customer engagements and feed field evidence back to Product to inform what should be standardized, deployed more broadly, or retired
• Collaborate closely with Product Managers, AI Specialists, and Program Managers to scope problem statements with a laser focus on customer and business impact
• Travel to customer sites as needed (expect regular travel) to support scoping, prototyping, and go-live phases of an engagement, including in-person workshops and executive readouts

Who can apply

• 5+ years of experience in a software engineering role, with strong hands-on backend engineering and cloud infrastructure experience
• 1+ years of experience building production-grade agentic AI solutions
• Proficiency in backend languages such as Python, Java, Typescript and technologies such as Docker, Kubernetes and Kafka with comfort working across the stack
• Deep understanding of cloud platforms (AWS preferred), able to design and operate both multi-tenant and hybrid customer-cloud deployment models
• Strong experience with data engineering fundamentals — ingesting, cleaning, and normalizing messy, inconsistent customer data across disparate source systems
• Working knowledge of identity and access management, encryption/key management, and secure network patterns (VPC peering, PrivateLink, mTLS) for customer-embedded or regulated environments
• Demonstrated comfort operating directly with customers — scoping ambiguous problems, running discovery, and demoing work-in-progress solutions live, in person and remotely
• Willingness and ability to travel regularly to customer sites, domestically and occasionally internationally, as engagement needs require
• Prior experience leading projects and debugging complex issues with minimal supervision

Perks & benefits

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