Principal Engineer – AI & Technology Systems

About the Role

Lenskart’s R&D function is building the next generation of technology-led vision care systems, including AI-assisted eye testing, intelligent retail workflows, connected devices, automation systems, and software-led diagnostic experiences.

We are looking for a Principal Engineer – AI & Technology Systems to join the R&D function as a senior technical builder and systems leader. This role is meant for someone who has built real products, platforms, tools, or technology solutions and can take ambiguous R&D problems from concept to working prototype, pilot, and scalable implementation.

This is not a product management role. We are looking for a hands-on technical leader with strong software engineering depth, AI exposure, system architecture capability, and the ability to lead multidisciplinary execution across software, AI, data, devices, and real-world workflows.

Role Objective

The person will act as a senior technical owner within the R&D function, helping convert high-potential ideas into robust, testable, and deployable technology systems.

The ideal candidate should be able to

• Understand complex real-world problems
• Define the technical architecture of solutions
• Build and guide software/AI systems
• Lead engineers and external partners
• Validate prototypes in real operating environments
• Convert successful experiments into scalable systems

Key Responsibilities

AI & Technology Systems Architecture

• Define system architecture for AI-native and software-led R&D projects.
• Design scalable solutions involving backend systems, frontend interfaces, APIs, databases, AI/ML models, cloud/local deployment, and device integrations.
• Evaluate technical trade-offs across performance, reliability, latency, scalability, security, usability, and maintainability.
• Build systems that can work reliably in real-world retail, optometry, diagnostic, and consumer environments.
• Create architecture documentation, technical specifications, integration flows, and validation plans.

Applied AI and Intelligent Workflow Development

• Lead the development of AI-assisted workflows involving decision engines, conversational systems, speech-to-text, NLP, computer vision, recommendation systems, and human-in-the-loop intelligence.
• Work with AI/ML teams, software engineers, and domain experts to convert AI capabilities into usable products.
• Evaluate AI models and systems not only for accuracy, but also for latency, reliability, user experience, failure handling, and deployment feasibility.
• Build feedback loops for continuous improvement of AI-enabled systems.
• Ensure AI systems are practical, explainable where required, and aligned with real user workflows.

R&D Execution and Prototyping

• Convert ambiguous R&D goals into clear technical milestones, experiments, prototypes, and implementation plans.
• Build proof-of-concepts quickly while maintaining sound engineering judgment.
• Identify technical risks early and create mitigation plans.
• Lead rapid prototyping, technical validation, pilot deployment, and iteration.
• Work closely with the R&D Lead to prioritize ideas based on technical feasibility, business impact, scalability, and speed of execution.
• Ensure that successful prototypes are designed with a clear path toward productionization.

Software, Data, and Device Integration

• Guide development across backend, frontend, data pipelines, AI services, dashboards, control systems, and integration layers.
• Support integration with external devices, instruments, sensors, APIs, control interfaces, and third-party software systems.
• Create robust communication between software systems and physical-world workflows.
• Define data capture, storage, processing, and analytics requirements for R&D systems.
• Ensure systems are testable, observable, maintainable, and secure.

Technical Leadership

• Mentor and guide software engineers, AI/ML engineers, data engineers, and integration teams.
• Review technical designs, architecture decisions, code quality, and implementation approaches.
• Help build a high-performance engineering culture inside the R&D function.
• Bring clarity to complex technical discussions and drive decisions under ambiguity.
• Support hiring, evaluation, onboarding, and development of technical talent.
• Work with internal teams and external vendors to ensure strong execution quality.

Cross-Functional Collaboration

• Collaborate with R&D, product, business, retail operations, optometry, design, data science, and engineering teams.
• Translate business and operational needs into robust technical solutions.
• Communicate complex technical topics clearly to technical and non-technical stakeholders.
• Work closely with leadership to evaluate new technology opportunities and build strategic R&D capabilities.
• Support partnerships, vendor evaluations, technology scouting, and external development collaborations where needed.