This is an emerging space where the technology, developer expectations, and best practices are changing quickly. We are looking for a technical leader who is energized by that uncertainty: someone who is willing to experiment, prototype, challenge established approaches, learn quickly, and form strong technical opinions based on evidence. You will explore new capabilities across LLMs, AI agents, reasoning systems, context and retrieval, evaluation, and intelligent automation, while determining which ideas can deliver meaningful and durable improvements to developer productivity.
Success requires more than applying existing AI technologies. You will identify where AI can fundamentally change engineering workflows, develop a technical point of view on where we should invest, and define the architectures and platform capabilities needed to move promising ideas from experimentation to production at OCI scale.
As a Lead Principal Engineer, you will influence the technical direction of AI-powered developer experiences across teams and organizational boundaries. You will work with senior engineers, engineering leaders, product managers, AI/ML teams, researchers, and OCI service organizations to identify opportunities, test hypotheses, establish architectural direction, and build alignment around consequential technical decisions.
You will remain deeply hands-on. You will build prototypes, evaluate emerging technologies, design foundational systems, and work alongside engineers to prove what is possible. Equally important, you will know when an experiment has provided enough evidence to invest, pivot, or stop—and how to turn successful experimentation into reliable, secure, scalable platform capabilities.
Internal Responsibilities
Responsibilities
- Build the architecture and technical strategy for AI Acceleration across Developer Tools, identifying where AI can materially improve developer productivity and establishing the foundational platforms, abstractions, and services required to enable those experiences.
- Experiment aggressively and learn quickly by building prototypes, evaluating emerging models and agentic technologies, testing unconventional approaches, and using measurable results to determine which ideas should progress from exploration to production.
- Create clarity from ambiguity by identifying high-value problems, forming technical hypotheses, challenging assumptions, evaluating competing approaches, and translating rapidly evolving AI capabilities into actionable architectural direction.
- Influence technical direction across the org building alignment among senior engineers, engineering leaders, AI/ML teams, product organizations, and service teams around architectural choices and strategic investments without relying on direct authority.
- Architect platforms for AI agents, LLM-powered developer experiences, context and retrieval, tool integration, orchestration, evaluation, and intelligent automation, with clear separation between rapidly evolving AI technologies and durable platform abstractions.
- Establish a disciplined path from prototype to production, defining engineering patterns that allow successful experiments to evolve into secure, observable, reliable, cost-efficient, and scalable OCI services.
- Raise the technical bar for AI engineering through architecture reviews, reference implementations, evaluation frameworks, technical mentorship, engineering standards, and clear communication of complex architectural tradeoffs.
- Develop mechanisms to measure whether AI actually improves the developer experience, using evaluation, telemetry, experimentation, developer feedback, and productivity signals to guide technical decisions and future investment.
- Anticipate changes in models, agent architectures, developer workflows, and AI infrastructure, designing platforms that can evolve without requiring fundamental rearchitecture as the underlying technology changes.
- Serve as a senior technical voice for AI Acceleration and Developer Tools, shaping technical strategy and representing the organization in architecture reviews and cross-OCI technical forums.
Required Qualifications
- 12+ years of software engineering experience with a demonstrated track record of architecting complex production systems and providing technical leadership across multiple engineering teams.
- Strong technical foundation in distributed systems, cloud platforms, developer infrastructure, or large-scale backend systems, with the architectural depth to design secure, scalable, reliable, and extensible platforms.
- Demonstrated experience applying or evaluating LLMs, generative AI, AI agents, or related AI technologies, with a strong understanding of both their potential and the engineering challenges involved in building production AI systems.
- Proven ability to experiment in ambiguous and rapidly changing technical environments—forming hypotheses, building prototypes, evaluating results, learning from unsuccessful approaches, and turning promising ideas into clear technical direction.
- Demonstrated ability to influence architecture and engineering strategy across teams without direct authority, establish a strong technical point of view, and build alignment among senior technical and organizational stakeholders.
- Experience taking new technologies or platform capabilities from exploration and proof-of-concept through architecture, production deployment, scale, and adoption, while balancing speed of innovation with long-term engineering quality.
- BS or MS in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
Desired Qualifications
- Experience building AI-powered developer tools, coding agents, engineering assistants, intelligent testing or debugging systems, autonomous development workflows, or AI-driven developer productivity platforms.
- Deep practical knowledge of LLMs, agentic architectures, reasoning models, tool use, context engineering, retrieval, inference, orchestration, evaluation, and emerging AI software engineering patterns.
- Experience designing AI platform abstractions that accommodate rapidly changing models and capabilities while providing stable interfaces, security, observability, governance, and operational reliability.
- Track record of using experimentation and quantitative evaluation to make decisions about emerging technologies, including defining success criteria and determining when to scale, pivot, or discontinue an approach.
- Demonstrated ability to challenge conventional approaches and introduce new technical directions, while mentoring senior engineers and building organizational consensus around high-impact architectural decisions.
#LI-SP1
External Responsibilities
Responsibilities
- Build the architecture and technical strategy for AI Acceleration across Developer Tools, identifying where AI can materially improve developer productivity and establishing the foundational platforms, abstractions, and services required to enable those experiences.
- Experiment aggressively and learn quickly by building prototypes, evaluating emerging models and agentic technologies, testing unconventional approaches, and using measurable results to determine which ideas should progress from exploration to production.
- Create clarity from ambiguity by identifying high-value problems, forming technical hypotheses, challenging assumptions, evaluating competing approaches, and translating rapidly evolving AI capabilities into actionable architectural direction.
- Influence technical direction across the org building alignment among senior engineers, engineering leaders, AI/ML teams, product organizations, and service teams around architectural choices and strategic investments without relying on direct authority.
- Architect platforms for AI agents, LLM-powered developer experiences, context and retrieval, tool integration, orchestration, evaluation, and intelligent automation, with clear separation between rapidly evolving AI technologies and durable platform abstractions.
- Establish a disciplined path from prototype to production, defining engineering patterns that allow successful experiments to evolve into secure, observable, reliable, cost-efficient, and scalable OCI services.
- Raise the technical bar for AI engineering through architecture reviews, reference implementations, evaluation frameworks, technical mentorship, engineering standards, and clear communication of complex architectural tradeoffs.
- Develop mechanisms to measure whether AI actually improves the developer experience, using evaluation, telemetry, experimentation, developer feedback, and productivity signals to guide technical decisions and future investment.
- Anticipate changes in models, agent architectures, developer workflows, and AI infrastructure, designing platforms that can evolve without requiring fundamental rearchitecture as the underlying technology changes.
- Serve as a senior technical voice for AI Acceleration and Developer Tools, shaping technical strategy and representing the organization in architecture reviews and cross-OCI technical forums.
Required Qualifications
- 12+ years of software engineering experience with a demonstrated track record of architecting complex production systems and providing technical leadership across multiple engineering teams.
- Strong technical foundation in distributed systems, cloud platforms, developer infrastructure, or large-scale backend systems, with the architectural depth to design secure, scalable, reliable, and extensible platforms.
- Demonstrated experience applying or evaluating LLMs, generative AI, AI agents, or related AI technologies, with a strong understanding of both their potential and the engineering challenges involved in building production AI systems.
- Proven ability to experiment in ambiguous and rapidly changing technical environments—forming hypotheses, building prototypes, evaluating results, learning from unsuccessful approaches, and turning promising ideas into clear technical direction.
- Demonstrated ability to influence architecture and engineering strategy across teams without direct authority, establish a strong technical point of view, and build alignment among senior technical and organizational stakeholders.
- Experience taking new technologies or platform capabilities from exploration and proof-of-concept through architecture, production deployment, scale, and adoption, while balancing speed of innovation with long-term engineering quality.
- BS or MS in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
Desired Qualifications
- Experience building AI-powered developer tools, coding agents, engineering assistants, intelligent testing or debugging systems, autonomous development workflows, or AI-driven developer productivity platforms.
- Deep practical knowledge of LLMs, agentic architectures, reasoning models, tool use, context engineering, retrieval, inference, orchestration, evaluation, and emerging AI software engineering patterns.
- Experience designing AI platform abstractions that accommodate rapidly changing models and capabilities while providing stable interfaces, security, observability, governance, and operational reliability.
- Track record of using experimentation and quantitative evaluation to make decisions about emerging technologies, including defining success criteria and determining when to scale, pivot, or discontinue an approach.
- Demonstrated ability to challenge conventional approaches and introduce new technical directions, while mentoring senior engineers and building organizational consensus around high-impact architectural decisions.
#LI-SP1
