Product Lead Engineer

We are building an AI-driven SaaS platform and are seeking a Product Lead Engineer to design, build, and scale the system.

This is a hands-on role for someone who can architect complex systems, integrate modern AI technologies, and build production-ready cloud applications. You will work directly with leadership to define the technical foundation of a scalable, secure, multi-tenant platform.

Key Responsibilities

• Design end-to-end system architecture for a cloud-based SaaS product
• Define data flow, service boundaries, and integration patterns
• Architect and implement AI/LLM-powered workflows
• Design scalable, secure multi-tenant architecture
• Build backend services (Python preferred; FastAPI is a plus)
• Develop frontend dashboards and interfaces (React preferred)
• Architect and deploy cloud infrastructure (AWS preferred)
• Implement Infrastructure as Code (Terraform or similar)
• Design serverless and event-driven systems where appropriate
• Ensure reliability, performance, cost optimization, and observability
• Establish engineering standards and technical best practices
• Contribute hands-on to MVP delivery
• Help define and scale the future engineering team

Required Qualifications

• 4+ years of software engineering experience
• Strong system design and architecture background
• Experience deploying scalable systems on AWS (or similar cloud platforms)
• Hands-on experience with Infrastructure as Code (Terraform preferred)
• Experience integrating LLMs into production environments
• Backend development expertise (Python preferred)
• Frontend experience (React, HTML, CSS)
• Experience building and scaling SaaS applications
• Strong understanding of APIs, distributed systems, and cloud security best practices

LLM / AI-Specific Requirements

• Experience with context engineering (prompt structuring, state management, memory handling)
• Strong understanding of prompt design patterns and model behavior tuning
• Experience implementing Retrieval-Augmented Generation (RAG) architectures
• Familiarity with embeddings, vector databases, and semantic search
• Knowledge of LLM cost optimization (token control, caching, batching strategies)
• Experience managing latency and reliability in AI-powered systems
• Understanding of guardrails, output validation, and AI safety patterns
• Familiarity with evaluation frameworks for LLM quality and performance

Cloud & DevOps Expectations

• Experience designing scalable AWS architectures
• Proficiency with Terraform (or similar IaC tools) for reproducible infrastructure
• Understanding of CI/CD pipelines
• Experience with containerization (Docker)
• Knowledge of monitoring, logging, and observability best practices
• Strong grasp of IAM, secrets management, and data security controls

Preferred Experience

• Experience with real-time or event-driven architectures
• Familiarity with high-throughput data processing systems
• Experience designing multi-tenant SaaS environments
• Exposure to compliance-aware or data-sensitive applications

Who You Are

• Systems thinker with strong architectural judgment
• Hands-on builder who can move from concept to production
• Comfortable operating in early-stage, ambiguous environments
• Product-minded engineer balancing speed with scalability
• Interested in growing into a senior technical leadership role

Job Type: Full-time

Ability to commute/relocate:

• Rawalpindi Satellite Town: Reliably commute or planning to relocate before starting work (Preferred)

Application Question(s):

• Describe how you would architect a scalable, multi-tenant SaaS platform that processes high volumes of data and delivers near real-time insights to a dashboard.

Please outline:
Major system components.
Data flow.
How you would ensure scalability and reliability?
How you would isolate tenants securely?

• Explain how you would integrate an LLM into a production SaaS system while controlling cost, latency, and output reliability.

Please include:
Context engineering approach.
Use of embeddings / RAG (if applicable).
Caching or optimization strategies.
Guardrails or validation methods.

• Describe how you would design and deploy a production SaaS backend on AWS using Infrastructure as Code (Terraform preferred).

Please outline:
Core AWS services you would use.
How you would structure Terraform modules.
Environment separation (dev/staging/prod).
Security best practices.

Work Location: In person