Description
Principal Engineer: Platform, AI Enablement

Location: Remote, USA
Position Summary

The Principal Engineer serves as the Technical Lead for an AI Enablement Platform engineering pod responsible for designing, building, and evolving shared AI platform capabilities that enable application teams across Frontline to safely, reliably, and efficiently operationalize AI and agentic workflows within customer-facing products and internal platform capabilities.

AI Enablement teams create foundational platform capabilities that provide safe, governed, observable, explainable, and easy-to-adopt AI services for downstream engineering teams. These capabilities include AI orchestration frameworks, agentic workflow infrastructure, provider abstraction layers, governance controls, observability tooling, reusable integration patterns, and operational AI enablement services.

This role provides technical leadership, implementation guidance, and architectural stewardship for the pod while remaining actively engaged in hands-on engineering activities including software design, prototyping, implementation, troubleshooting, operational support, experimentation, and platform modernization.

The Principal Engineer partners closely with Engineering Managers, Product Managers, Platform Architects, QA Engineers, Security teams, Data Platform teams, and peer Technical Leads to ensure AI platform solutions align with Frontline architectural standards, governance expectations, security requirements, operational maturity goals, and long-term platform strategy.

The role participates throughout the product lifecycle including discovery, refinement, implementation, delivery, adoption enablement, operational support, and continuous improvement activities.
Operating Model Expectations

Engineering teams within Platform operate within a product-oriented delivery model emphasizing customer outcomes, continuous discovery, shared ownership, and cross-functional collaboration.

The Principal Engineer is expected to:

• Participate in product discovery and technical solution shaping activities.
• Collaborate with Product Management and stakeholders to evaluate feasibility, tradeoffs, governance implications, operational risks, and delivery approaches.
• Take ownership of technical outcomes, operational sustainability, platform adoption success, and long-term maintainability.
• Promote continuous improvement across engineering practices, developer experience, AI operational maturity, and platform capabilities.
• Balance immediate delivery objectives with long-term scalability, governance, observability, explainability, security, reliability, and cost efficiency.
• Drive alignment with Frontline architectural standards, engineering patterns, governance expectations, and platform strategy.
• Help ensure AI platform capabilities are reusable, composable, observable, explainable, and easy for downstream teams to adopt.
• Act like an owner by proactively identifying opportunities, risks, governance gaps, and improvements that advance broader organizational goals.

Key Responsibilities
Technical Leadership & Pod Execution

• Serve as the Technical Lead for an AI Enablement Platform engineering pod responsible for shared AI platform capabilities and agentic workflow infrastructure.
• Provide day-to-day technical leadership, guidance, and mentorship to engineers within the pod.
• Lead technical execution activities including decomposition, implementation strategy, design reviews, and delivery coordination.
• Partner with Engineering Managers to ensure successful sprint execution and continuous improvement.
• Collaborate with Platform Architects to align solutions with Frontline architectural patterns, standards, governance expectations, and long-term platform strategy.
• Promote engineering craftsmanship with strong emphasis on scalability, reliability, observability, explainability, security, governance, maintainability, and operational excellence.
• Facilitate technical discussions and effectively drive alignment across stakeholders and engineering teams.
• Help establish reusable implementation approaches and operational AI platform patterns that improve consistency across the organization.

Product Discovery & AI Platform Solution Design

• Collaborate closely with Product Managers and stakeholders during discovery activities to shape reusable platform-oriented AI solutions.
• Participate in early-stage ideation, technical feasibility analysis, rapid prototyping, and platform capability planning.
• Help identify assumptions, dependencies, governance implications, operational risks, explainability concerns, and adoption challenges before delivery commitments are made.
• Contribute to defining MVP scope and iterative delivery strategies.
• Ensure solutions are designed with downstream developer experience, governance, operational visibility, and adoption simplicity in mind.
• Participate in refinement sessions to ensure requirements are technically sound, testable, extensible, and operationally sustainable.

AI Enablement Platform Engineering

• Design and implement shared AI platform capabilities enabling operational AI and agentic workflows across Frontline applications.
• Develop and maintain orchestration services, integration layers, workflow infrastructure, and reusable platform APIs supporting AI-enabled product development.
• Build platform capabilities supporting provider abstraction, policy enforcement, observability, auditing, telemetry, explainability, and governance.
• Contribute to operational AI workflow patterns including agent orchestration, tool integration, retrieval workflows, and asynchronous execution models.
• Design and support event-driven and distributed systems leveraging Kafka and related messaging technologies.
• Collaborate with downstream engineering teams to simplify AI capability adoption and reduce integration friction.
• Contribute to operational readiness including monitoring, resiliency, troubleshooting, incident response, governance enforcement, and platform reliability.
• Support modernization and evolution of AI platform capabilities as technologies and operational requirements mature.

Responsible AI & Operational Governance

• Help establish safe, governed, and trustworthy AI platform capabilities supporting enterprise operational requirements.
• Contribute to patterns supporting explainability, observability, auditing, access controls, and responsible AI operational practices.
• Collaborate with Security, Architecture, Data Platform, and application teams to ensure AI platform capabilities align with organizational governance expectations.
• Help teams make pragmatic decisions balancing innovation, operational maturity, governance requirements, and customer value.
• Evaluate emerging AI and agentic technologies with focus on operational viability, interoperability, governance, maintainability, and long-term platform fit.

Architecture, Standards & Engineering Excellence

• Lead Design Sketch reviews and contribute solution-level architecture documentation.
• Ensure solutions align with established platform standards, governance expectations, architectural patterns, and engineering best practices.
• Drive adoption of modern CI/CD, automated testing, and operational excellence practices.
• Champion secure-by-default, observable-by-default, and governable-by-default engineering practices.
• Mentor engineers through design discussions, code reviews, pair programming, and technical coaching.
• Promote reusable platform capabilities and encourage consistency across product teams.
• Help establish engineering practices that improve long-term maintainability, platform adoption, and developer productivity.

AI-Assisted & Agentic Engineering Practices

• Effectively leverage modern AI-assisted and agentic development tooling such as GitHub Copilot, Claude Code, and OpenAI Codex to improve engineering productivity and accelerate delivery.
• Apply sound engineering judgment when supervising, validating, and operationalizing AI-generated outputs.
• Help establish practical and responsible AI-assisted engineering workflows that maintain strong standards for maintainability, scalability, reliability, governance, security, explainability, and operational integrity.
• Encourage experimentation and continuous improvement in engineering practices while maintaining strong delivery discipline and human accountability.

Qualifications
Required

• Bachelor's Degree in Computer Science or related field.
• 10+ years of professional software engineering or platform engineering experience.
• Proven experience leading technical execution for engineering teams or pods.
• Strong experience designing and building distributed cloud-native systems.
• Experience designing and implementing operational platform capabilities supporting AI-enabled workflows.
• Strong understanding of:
• Distributed systems and asynchronous workflows
• Event-driven architectures
• API and integration platform design
• Observability and operational telemetry
• Security and governance patterns
• AI orchestration and workflow concepts
• Experience with AWS cloud-native development including services such as:
• Lambda
• EC2
• S3
• SNS/SQS
• Container-based workloads
• AI or data-related services
• Experience with:
• Kafka or equivalent messaging technologies
• Distributed systems and workflow orchestration concepts
• Docker
• Modern API and microservice architectures
• Familiarity with operational AI patterns including orchestration, retrieval workflows, agentic execution models, or provider abstraction concepts.
• Experience operating within Agile/Scrum delivery models.
• Strong communication skills with the ability to engage technical and non-technical stakeholders.
• Demonstrated ability to mentor engineers and elevate engineering practices across teams.

Preferred

• Experience with AWS Bedrock or similar enterprise AI platforms.
• Experience designing shared platform capabilities consumed across multiple product teams.
• Experience with observability, governance, auditing, or telemetry platforms.
• Familiarity with vector search, embeddings, retrieval workflows, or AI integration patterns.
• Experience supporting AI or agentic enablement in enterprise application environments.
• Familiarity with Kubernetes or container orchestration platforms.
• Experience collaborating with offshore or geographically distributed engineering teams.
• Experience leveraging AI-assisted or agentic development workflows in professional software engineering environments.

Personal Attributes

• Acts like an owner by taking accountability for outcomes, platform adoption, governance, operational sustainability, and long-term maintainability.
• Strong systems-thinking mindset that balances innovation with operational maturity and enterprise-wide impact.
• Pragmatic and delivery-oriented while maintaining high engineering and governance standards.
• Comfortable operating in ambiguity and helping teams create clarity.
• Strong collaborator who values partnership, transparency, and shared success.
• Passionate about platform enablement, operational AI systems, reusable engineering solutions, and developer experience.
• Curious, adaptable, and continuously learning.
• Comfortable operating across a broad range of platform engineering responsibilities rather than narrowly specialized domains.
• A "One Team" mindset grounded in servant leadership and shared accountability.