Principal Enterprise Software Engineer

Description

The company is hiring a hands-on principal engineer to design, build and deploy enterprise-scale AI solutions on its next-generation AI platform, working inside the Architecture & Engineering Services team.

You will work with the Engineering team and with customers, Cloud Operations, Security, Compliance and business stakeholders to drive AI-powered change across the enterprise.

The role centres on cloud-native, agentic AI solutions that automate decisions and processes, built from agents, orchestrations, skills and plugins. You will also turn business processes into AI-driven workflows and help shape the platform roadmap.

Responsibilities

• Turn business processes and operational workflows into AI use cases built from agents, orchestrations, skills and plugins on the platform.
• Lead design and delivery of end-to-end AI solutions that combine process engineering with business logic.
• Design agentic systems that pair intent-driven decisions and dynamic authorization with controlled execution.
• Spread AI adoption across the organization by letting business units and engineering teams build, deploy and run intelligent workflows without deep AI expertise.
• Mentor the engineering team on AI solution design patterns, governance frameworks and best practices.
• Help shape the AI platform roadmap using customer needs, operational requirements and emerging patterns.
• Work with stakeholders to define AI governance, compliance and risk controls for enterprise deployments.
• Build reusable, composable skills, orchestrations and patterns that speed up team delivery and shorten time-to-value for others.
• Maintain a clear picture of both the technical depth of AI systems and the business context they serve.

Requirements

• Motivation: a drive to change enterprise operations with AI agents and automation, and to make advanced AI usable by business units and non-expert engineers.
• Comfort designing and shipping under pressure and ambiguity, with a practical, can-do approach.
• Strong grasp of cloud and hybrid environments, with experience architecting on Azure, AWS or GCP.
• Deep knowledge of distributed systems, microservices and cloud-native patterns.
• Proven experience in high-level languages; Python preferred, Java or C acceptable.
• Solid fundamentals in operating systems, storage, networking and infrastructure.
• Proven record building production LLM applications and agentic systems.
• Deep experience designing and orchestrating AI agents that decide, act and run workflows on their own.
• Strong Python skills and experience with AI frameworks and SDKs such as LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK or equivalent.
• Understanding of intent-driven decisions, dynamic authorization and controlled execution.
• Experience building reusable, composable AI components such as skills, orchestrations and plugins for many teams.
• Ability to turn business processes into AI-solvable use cases and break complex workflows into orchestrations, skills and plugins that work together.
• Familiarity with process automation platforms, workflow engines and integration patterns.
• Experience working with business stakeholders to find automation opportunities and design intelligent workflows.
• Expert API design and consumption skills, with strong async and event-driven architecture experience.
• Experience designing scalable integrations that connect AI solutions to enterprise systems, including hybrid and multi-cloud setups.
• Strong background testing AI and ML systems (unit, integration, evaluation), with familiarity in model benchmarking and validation.
• Deep experience designing AI governance frameworks, policy enforcement layers and risk classification models.
• Skill in building audit trails, compliance controls and observability for regulated industries.
• Commitment to quality and to continuously improving solutions, processes and platform capabilities.
• Deep experience with Azure services such as Azure OpenAI, Azure AI Services, Cosmos DB, Functions and Azure Cognitive Search, or equivalent AWS or GCP services.
• Understanding of data pipelines, vector databases and knowledge graph construction.
• Experience designing data governance and knowledge management for AI systems.
• Comfort working across functions with business stakeholders, customers, security and compliance teams, translating between business problems and technical solutions.
• Ability to grow the engineering team's AI skills and design patterns, and to lead thinking on AI architecture, governance and best practice.
• Willingness to prototype, test and ship quickly, and to fix bugs and improve existing solutions.
• Comfort with ambiguity and new AI patterns, and eagerness to try new approaches.
• Resilience and a practical attitude toward technical and organizational complexity.
• Design and deliver a multi-year plan to scale AI from pilots to production across the enterprise.
• Create reusable patterns, reference architectures and best practices that speed up team delivery.
• Mentor and train engineers on agentic systems, orchestrations and AI solution design.
• Define AI solution maturity models that help teams judge readiness and reach value sooner.
• Teach teams to build with composable skills, orchestrations and plugins that hide complexity.
• Use the AI platform as a governed sandbox for learning and experimentation, and run hands-on workshops, labs and playbooks that take teams from business problem to deployed solution.
• Build and maintain solution templates and reference implementations for learning and for new projects.
• Document and promote emerging patterns, governance frameworks and best practices across the organization.
• Research and implement new AI technologies and approaches, such as agentic orchestration, safety patterns and cost optimization, that improve resilience, scalability and economics.
• Run proofs of concept and evaluate new LLM models, frameworks and cloud services, including Azure OpenAI and newer optimization techniques.
• Measure engineering velocity, solution quality and AI governance maturity, and quantify the business impact of platform investment.
• Write engineering design artifacts and functional and design specifications that turn business workflows into agentic solutions.
• Implement and promote AI safety patterns such as output validation, escalation gates, controlled execution and rollback across all solutions.
• Design AI governance frameworks aligned with NIST AI RMF and ISO/IEC 42001 for regulated sectors such as healthcare, financial services and legal tech.
• Make sure all AI deployments meet SOC 2, ISO 27001, HIPAA and other applicable regulatory requirements.
• Architect and deliver agentic orchestration systems, including multi-agent systems, tool use, long-running workflows and intent-driven decisions.
• Build AI safety and governance patterns that make agent autonomy responsible and auditable.
• Optimize production LLM applications for cost and speed using prompt caching, model quantization and intelligent batching.
• Design solutions for regulated industries that meet strict compliance and audit demands.
• Take part in strategic planning to shape the platform roadmap from emerging needs, customer feedback and industry trends.
• Propose and promote new processes, methods and governance approaches that make AI solutions more resilient, economical and trustworthy.
• Join industry discussion on AI safety, governance and responsible deployment through open-source work, research or talks.
• Pilot new technologies, check cloud provider offerings and propose platform improvements that build capability and AI maturity.
• Education: a Bachelor's degree in Computer Science, Information Systems or a related technical field is required.
• Education: a Master's degree in Computer Science, Engineering, Business Administration or a related field is strongly preferred.
• Education: a PhD in Computer Science, Machine Learning or a related discipline is a plus.
• Interviews must be completed without AI tools or outside prompts, with virtual backgrounds removed. Use of AI-generated answers or third-party help during interviews is grounds for disqualification.
• You may be required to attend an interview onsite at a the company office.

Conditions

Base salary range: $133,400.00 to $238,400.00 USD, based on the primary location of the role.

This role is eligible for a bonus.

Actual base offer depends on skills, experience and the hiring location. The recruiter can give details for the specific location during the process.

Equal-opportunity statement omitted for brevity.

Benefits

• Medical, dental and vision plans, 401(k), FSA and HSA, commuter benefits, tuition assistance, vacation and sick time, and paid parental leave.
• Full benefits details are available on request.