- 11+ years in software engineering with a strong track record of shipping production systems - not prototypes, not internal tools, but systems that served real customers under real load.

- At least 4-5 years managing and mentoring engineers while remaining deeply technical.

- You have grown engineers, not just assigned them tickets.

- Expert-level system design capability.

- You have designed systems under real constraints - high throughput, low latency, partial failure, and evolving requirements.

- You produce clear, reasoned architecture documentation and can defend every decision under scrutiny.

- Deep microservices architecture experience.

- You have designed, decomposed, and operated microservices at production scale - you understand service boundaries, distributed consistency, API contracts, versioning, and the real cost of over-decomposition.

- Event-driven architecture in production.

- You have owned Kafka at scale - topic design, partitioning, consumer group management, Schema Registry, Debezium CDC, and debugging consumer lag and ordering issues in live systems.

- Event-driven is not a pattern you have read about; it is how you build.

- Deep Node.js expertise - event loop, async patterns, performance profiling, and production debugging.

- You have shipped high-throughput Node.js services and know where the runtime bites you.

- React / Next.js proficiency - You can meaningfully review frontend architecture, SSR vs CSR trade-offs, state management, and component design.

- You do not need to be a CSS expert, but you cannot be blind to the frontend.

- MongoDB in production - schema design, indexing strategy, aggregation pipelines, sharding trade-offs, and performance tuning under load.

- AWS architecture experience - ECS/EKS, Lambda, RDS, S3 networking (VPC, security groups), and cost optimisation.

- You can design a new service's infrastructure from scratch without a dedicated DevOps team.

- DevOps and SRE practices - CI/CD, IaC (Terraform), Kubernetes, observability (tracing, metrics, and logging), on-call rotation, and incident management.

- You have owned production reliability personally.

- Comfortable being a polyglot.

- Rust is on the stack.

- You do not need to be an expert on day one, but you learn new languages fast and are curious, not resistant.

- Apache Camel working knowledge - mediation, routing, and transformation patterns well enough to review integration designs, evaluate BOT scraping architectures, and debug pipeline failures.

- Clear, direct communication - you articulate architectural trade-offs to senior leadership, push back on product requests with data, and give engineers feedback that makes them better.

Preferred :

- Domain knowledge in supply chain, procurement, or building data-intensive marketplace platforms.

- Rust experience in production services, CLI tooling, or performance-critical components.

- Experience with ClickHouse or OpenSearch at scale - data modelling, query optimisation, and cluster management.

- Exposure to data engineering pipelines (Airflow, Spark, dbt) at a level sufficient to review architecture and unblock data engineering leads.

- Experience with cost optimisation on AWS at a meaningful scale - right-sizing, Reserved Instances, Savings Plans, and data transfer cost reduction.

Nice to Have :

- Exposure to data modelling for analytics and BI - understanding the difference between OLTP and OLAP schema design and when each is appropriate.

- Familiarity with privacy and compliance concepts (GDPR, India DPDP) at an implementation level - field-level encryption, data retention pipelines, audit logging.

- Experience with GraphQL federation or API gateway patterns for multi-service frontends.

- Contributions to open-source projects or a visible engineering portfolio (blog, talks, GitHub).