Company Description Aleph-Labs is a creative engineering company headquartered in Singapore, serving clients across the Asia Pacific region with financial, internet, mobile, and desktop solutions. With nearly 300 team members, Aleph-Labs has grown from a small startup into a borderless organization that maintains a robust, collaborative culture across markets. The company is known for its ability to bridge design and technology, delivering functional, flexible, and user-centric products. Aleph-Labs focuses on creating applications that people love to use and that clients are proud to own, approaching each challenge as an opportunity to apply its diverse skills and expertise. Role Description About Natural IntelligenceNatural Intelligence builds AI products that turn complex, evolving data into useful knowledge and decisions. Our work spans Aura’s multimodal estate intelligence and complex claims workflows, bringing together structured records, documents, photos, video, audio and live sensor data. We are looking for a hands-on engineer to own the secure, reliable platform and backend that make these products work. Your priorities are platform engineering first, backend product development second, and data infrastructure third. You will work directly with our founder and data lead/product owner to turn product priorities into production systems. This is a builder role in a lean startup. You will make architectural decisions, write and deploy code, operate what you ship, and proactively resolve problems. AI-native development is fundamental to the role—not an optional productivity tool. What you will ownGCP platform and production reliability
• Own production infrastructure on Google Cloud Platform: infrastructure-as-code, environment configuration, networking, access controls and cost management.
• Build automated CI/CD pipelines with meaningful tests, controlled releases, rollback paths and reproducible deployments. Choose managed services where they reduce operational burden.
• Establish actionable logs, metrics, traces, health checks and alerts. Define service objectives, test backups and recovery, and remove recurring sources of failure.
• Own incident response and participate in after-hours production on-call. Maintain runbooks, communicate incidents clearly and turn post-incident findings into lasting improvements. Backend services and AI product delivery
• Design and implement maintainable backend services, business logic, integrations and documented APIs for frontend experiences and AI workflows.
• Build reliable asynchronous and event-driven workflows, including queues, retries, idempotency, timeouts, rate limiting and failure recovery.
• Productionize AI capabilities with the data lead: model and agent integrations, retrieval services, tool interfaces and background processing, with evaluation hooks, tracing and cost controls.
• Support secure multi-tenant products with robust authentication, authorization and data-access boundaries. Define API contracts and acceptance criteria with the frontend engineer and data lead/PO.



Streaming and data infrastructure
• Build dependable batch and streaming ingestion for APIs, documents, media, sensor feeds and operational systems. Handle duplicates, late events, schema changes, backpressure and replay.
• Implement storage and lake/lakehouse foundations, orchestration, data contracts, lineage and ingestion-quality checks. Support large media uploads and processing without blocking interactive services.
• Own the reliability of ingestion, storage and orchestration. Partner with the data scientist, who owns analytical transformations, feature engineering, models and insight discovery. Security by design
• Implement least-privilege IAM, secure service identities, tenant isolation, encryption, secrets management and auditable access to sensitive data.
• Apply secure coding, threat modelling, dependency scanning, vulnerability remediation and tested authorization controls throughout delivery—not only before launch.
• Protect sensitive claims documents and other customer information through appropriate retention, deletion, logging and access policies. Build practical compliance readiness and maintain evidence of security controls.
• Secure AI integrations and agent tools: constrain permissions, validate inputs and outputs, treat retrieved content as untrusted, and prevent sensitive data exposure through prompts, logs or development tools. AI-native engineering is a requirementYou must already have experience building AI products and developing backends through agentic coding workflows. Using a chatbot occasionally or relying only on autocomplete is not enough.
• Use coding agents such as Claude Code, Codex, Cursor or equivalent tools routinely for implementation, testing, debugging, refactoring, infrastructure changes and documentation.
• Break work into bounded tasks, provide repository context and clear acceptance criteria, and build repeatable workflows that make the whole team faster.
• Review generated code, verify behaviour with automated tests and inspect security implications. Apply appropriate approval boundaries to production changes and privileged actions.
• Remain accountable for every shipped change. Demonstrate improved delivery speed without sacrificing correctness, maintainability or security. Required experience
• 5+ years of relevant backend, platform or software engineering experience, with direct ownership of production systems.
• Hands-on production GCP experience, including deployment, IAM, networking, storage, monitoring and incident troubleshooting—not certification alone.
• Experience shipping AI-enabled products and personally building backend systems using coding agents and agentic development workflows.




• Strong backend programming skills, API design, SQL and database modelling; practical experience with automated testing, containers, infrastructure-as-code and CI/CD.
• Experience with streaming or event-driven systems, asynchronous processing and reliable data pipelines.
• Practical application and cloud security experience, including authentication, authorization, tenant isolation, secrets and sensitive-data handling.
• The independence and organization to prioritize ambiguous work, communicate trade-offs, surface risks early and deliver without daily prompting.
• Willingness to own production operations, including after-hours on-call and incident response. Especially valuable
• Experience with GCP services such as Cloud Run, GKE, Pub/Sub, Cloud Storage, BigQuery, Cloud SQL, Dataflow and Secret Manager, or comparable patterns. You need sound service-selection judgement, not every tool on the list.
• Python backend experience and close collaboration with data science teams; experience serving models, retrieval systems or agent tools through production APIs.
• Work with sensitive insurance/claims data, multimodal media pipelines, IoT streams or enterprise multi-tenant products.
• Evidence of simplifying architecture, controlling cloud and AI inference costs, and improving reliability in a small, fast-moving team. What success looks like in the first 90 daysAgree priorities and measurable acceptance criteria with the founder and data lead/PO, then deliver across the following areas:
• Assess and stabilize the existing backend and GCP foundation, addressing the highest-risk reliability and security gaps.
• Ship secure, documented APIs and priority integrations that support real frontend and AI product workflows.
• Establish dependable ingestion and a usable data storage foundation, including streaming where needed, clear contracts and operational monitoring.
• Put repeatable agent-assisted engineering, tested deployments, incident runbooks and recovery procedures into daily use. What we would like to seeShow us an AI product backend or platform you personally shipped and operated. Explain your architectural choices, GCP responsibilities, security controls, a production incident you handled and the results you achieved. Walk us through how you use coding agents to develop a backend: task decomposition, context, generated changes, verification and deployment. Include a case where you caught an incorrect or unsafe suggestion. Share a GitHub repository, sanitized code sample or technical walkthrough where possible. Private employer work is understandable; do not disclose confidential code, credentials or customer data. Work arrangementFull-time and remote within India, with occasional company-paid travel. You will work directly with our founder and data lead/product owner, collaborating closely with frontend/design and data science. The role includes after-hours production on-call; coverage and escalation arrangements will be discussed during hiring. •

📌 Senior Platform Engineer (India)
🏢 Aleph-Labs
📍 India