Principal AI & DevOps Engineer

Job Title

Principal AI & DevOps Engineer

Location(s)

Mumbai

About Us

Revvity is a developer and provider of end-to-end solutions designed to help scientists, researchers, and clinicians solve the world’s greatest health challenges. We pair the enthusiasm of an industry disruptor with the experience of a longtime leader. Our team of 11,000+ colleagues from around the globe are vital to our success and the reason we’re able to push boundaries in pursuit of better human health.

Find your future at Revvity 

Are you a seasoned technologist who thrives at the intersection of artificial intelligence and business transformation? Do you have a proven track record of turning complex AI/ML concepts into scalable, production-grade systems that move the needle for enterprise operations? If so, we want to hear from you.

We are looking for a Principal AI & DevOps Engineer to join our team — a strategic builder and technical leader who brings deep expertise, sharp instincts, and a bias for action. You won't just execute; you'll shape the direction of how we architect, deploy, and scale AI/ML solutions across the organization.

 

What You'll Own

First 30 Days — Strategic Discovery & Architecture Assessment

Enterprise Systems Audit: Rapidly assess our existing technical landscape, business architecture, and active AI workstreams — bringing your experience to quickly identify gaps, redundancies, and high-leverage opportunities others might miss.

 

Cross-Functional Stakeholder Engagement: Lead structured discovery sessions across business units to surface operational friction points and define a prioritized roadmap for AI/ML intervention — drawing on your experience translating business pain into technical solutions.

 

Technology Evaluation & Benchmarking: Apply your deep knowledge of emerging AI/ML technologies, industry trends, and software engineering best practices to evaluate our current toolchain and recommend improvements with clear rationale.

 

Strategic Value Mapping: Deliver a well-reasoned assessment of where generative AI can reduce manual overhead, unlock creative capacity, or create competitive advantage — backed by your own experience doing exactly that.

 

Beyond 30 Days — Build, Lead, and Scale

Full-Stack AI Application Development: Architect and deliver production-quality, full-stack AI-powered applications — leveraging Python backends and JavaScript/Flutter frontends — with a focus on performance, maintainability, and user experience informed by years of hands-on delivery.

Context Engineering & LLM Optimization: Design and implement sophisticated context engineering strategies — orchestrating enterprise data, memory systems, tool outputs, and prompt chaining within LLM context windows to produce accurate, structured, and reliable outputs at scale.

 

End-to-End Pipeline Ownership: Own the full deployment lifecycle. Design, implement, and continuously improve CI/CD pipelines for LLM applications — including automated testing frameworks — applying best practices you've refined over your career.

 

Data Engineering & ML Lifecycle Management: Drive data quality, pipeline integrity, and dataset governance to fuel deployed ML models — bringing mature engineering discipline to data validation, query optimization, and model input management.

Observability & Performance Engineering: Establish robust monitoring frameworks using tools like AWS CloudWatch, define and track AI performance against business KPIs, and deliver executive-ready dashboards and reports that connect system health to business outcomes.

 

Technical Leadership & Knowledge Sharing: Mentor peers through code reviews, lead architectural discussions, and present fully operational solutions during stakeholder demos — translating complex AI/ML architecture into clear, compelling narratives for both technical and non-technical audiences.

Required Qualifications

 

• Bachelor's or Master's degree in Computer Science or equivalent field
• 8–12 years of hands-on experience in full-stack development
• Proven DevOps & Automation expertise: Deep experience with CI/CD tooling, deployment workflows, and Infrastructure as Code (IaC) in production environments

 

This role is designed for someone who brings their own perspective, methodology, and technical philosophy — not just executes a playbook. We value engineers who have strong opinions, loosely held, and the experience to back them up.