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Job description
Location: India; Delhi NCR; Indore; Bangalore; Hyderabad; Chandigarh; Pune
About Smart Working
At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being. Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.
Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.
About the Role
As a DevOps / Platform Engineer, you will play a key role in supporting and scaling the cloud infrastructure behind an AI-native cybersecurity platform that processes terabytes of data daily. You will take end-to-end ownership of critical infrastructure and contribute to the reliability, scalability and operational efficiency of the platform.
You will join an engineering team of 14+ professionals across Backend, DevOps, Endpoint, Frontend and Design, reporting to the VP of Engineering and AI. The team operates with a flat structure and a single-owner model, where engineers independently own their discipline within each project while collaborating closely with others.
This role suits a mid-to-senior engineer who thrives on autonomy, embraces AI-assisted engineering and wants to deliver high-impact solutions.
Responsibilities
Run, maintain and scale production cloud infrastructure supporting a platform that processes terabytes of data daily.
Own build, deployment and release pipelines for Python, C++ and TypeScript applications.
Build and manage production infrastructure for AI agents, incorporating observability, guardrails and cost controls.
Take ownership of observability and incident response to maintain platform reliability.
Automate repetitive operational work using AI agents.
Take end-to-end ownership of infrastructure and platform engineering projects, from identifying requirements through to delivery.
Manage multiple priorities, including two major projects alongside smaller AI-driven tasks, while maintaining effective communication and collaboration.
Requirements
Deep, hands-on experience managing production cloud infrastructure, with expertise in AWS, Microsoft Azure or Google Cloud Platform (GCP).
Practical experience with Infrastructure as Code (IaC), using Terraform or equivalent technologies.
Hands-on experience with Docker and containerised environments.
Proven ownership of CI/CD pipelines, including build, deployment and release processes.
Experience implementing and managing observability, incident response and rigorous security practices.
Daily, hands-on use of AI agents and frontier AI models as part of engineering workflows.
Demonstrated ability to use LLMs and AI agents autonomously to learn unfamiliar technologies, navigate unfamiliar codebases and manage multiple projects effectively.
Ability to independently own engineering projects from start to finish rather than simply completing assigned tickets.
Strong customer focus, including willingness to communicate directly with customers and take ownership of solutions until customer problems are resolved.
A high sense of urgency, with the judgement to reprioritise work and focus on delivering the greatest impact.
Ability to set priorities independently, make decisions and deliver results without close supervision while maintaining consistent communication and collaboration.
A growth mindset, with openness to constructive criticism, willingness to discard or rethink existing code and projects, and commitment to improving the wider team.
A trustworthy and accountable approach, demonstrating honesty, attention to detail, early identification of problems and consistent delivery.
Ability to explain technical decisions, implementation approaches and engineering solutions clearly, demonstrating genuine technical understanding.
Ability to work remotely during UK working hours.
Nice to Have
Experience with Microsoft Azure, particularly relevant to cloud migration initiatives and Azure Marketplace integration.
Experience working with ClickHouse or other data-intensive platforms.
Experience running Large Language Model (LLM) or AI agent workloads in production.
Scripting experience with Python or Go.
Familiarity with SOC 2, GDPR or other regulated environments.
This page is for Indian job seekers preparing for cloud engineer, DevOps, backend and solutions architect roles that list AWS as a skill, whether you are a fresher with a first certification or an experienced engineer moving into a senior role. It covers 60 questions in three levels: core services and concepts, then design and configuration choices for one to three years of experience, and finally scenario, security, cost and troubleshooting questions asked in senior rounds. Read each answer, explain it aloud in your own words, and try the CLI commands in a practice account with a billing alert set.
Questions you may be asked
What is the difference between an AWS Region and an Availability Zone?
Explain the AWS shared responsibility model.
What is Amazon EC2, and how do you read an instance type name like m7g.large?
What is Amazon S3, and what are its main storage classes?
