About the Role :

We are looking for a highly experienced Principal Engineer to define technical direction, architecture, and engineering standards across the product portfolio and internal platforms.

The role will focus on scalable backend systems, cloud platforms, data architecture, and AI/ML infrastructure.

Key Responsibilities :

- Define technical direction and architecture for products, platforms, and backend systems.

- Own long-term architecture decisions across distributed systems, data architecture, cloud infrastructure, and AI/ML platforms.

- Establish engineering standards for system design, backend development, scalability, reliability, security, and code quality.

- Design and build scalable backend services, APIs, microservices, and distributed systems.

- Drive the integration of AI/ML capabilities into products and engineering workflows.

- Build internal AI platforms covering LLMs, retrieval, inference, evaluation, and agentic workflows.

- Develop AI-assisted engineering tools, code-generation pipelines, automated QA, and developer productivity solutions.

- Work hands-on with foundation models, LLMs, embeddings, vector databases, RAG, and inference systems.

- Evaluate emerging AI technologies and translate relevant capabilities into production-ready solutions.

- Design AI infrastructure with a focus on performance, scalability, reliability, latency, and cost efficiency.

- Identify and address technical debt, scalability challenges, reliability issues, and engineering bottlenecks.

- Collaborate with engineering, product, and leadership teams to drive technical alignment and execution.

- Mentor senior engineers and influence engineering practices across teams.

- Stay hands-on by writing code, developing prototypes, and validating architectural decisions through working systems.

Technical Requirements:

- 12 - 16 years of software engineering experience with a strong foundation in hands-on development.

- Strong expertise in backend engineering and distributed systems.

- Extensive experience building scalable Cloud/SaaS products, enterprise platforms, or developer infrastructure.

- Strong proficiency in backend programming languages such as Go, Java, Python, or similar.

- Deep understanding of APIs, microservices, event-driven architecture, databases, caching, messaging, and distributed systems.

- Strong cloud and platform engineering experience, preferably with AWS.

- Experience with containers, Kubernetes, CI/CD, and cloud-native architectures.

- Strong understanding of AI/ML and Generative AI technologies.

- Hands-on experience with LLMs, foundation models, embeddings, vector databases, RAG, and inference systems.

- Experience integrating or building production AI/ML platforms and services.

- Understanding of model evaluation, AI observability, agentic systems, and retrieval infrastructure.

- Ability to design systems combining traditional distributed backend architecture with modern AI/ML infrastructure.

- Strong understanding of scalability, performance, reliability, security, and cost optimization.

Leadership & Technical Influence:

- Proven ownership of large-scale architecture and technical decisions in production environments.

- Ability to influence engineering leaders, product teams, and senior stakeholders.

- Strong technical judgment and ability to drive alignment across complex engineering environments.

- Track record of improving engineering practices, technical culture, and development velocity.

- Strong communication skills with the ability to explain complex technical concepts clearly to technical and executive stakeholders.

- Strong bias toward building, experimenting, and validating solutions through working systems.

Preferred Experience :

- Experience working in a B2B SaaS or enterprise software environment.

- Experience building AI-native products or internal AI engineering platforms.

- Experience with agentic AI, AI-assisted development, automated testing, or developer productivity platforms.

- Experience with vector databases, model-serving platforms, AI evaluation frameworks, or retrieval infrastructure.