Software Development Engineer II (Full Stack, Back)

We're looking for strong software engineers who own systems end to end, from design through production. We hire for deep fundamentals across services, data, APIs and frontend rather than any one language, and for a record of shipping platforms that hold up at scale, not prototypes. You'll use AI tools daily to move faster, without lowering the engineering bar.

Responsibilities:

• Own platform components end to end: design, build and run features from LLD and HLD through production, including availability, performance and cost.
• Work across the stack: backend services, databases, event pipelines, APIs and gateways, and frontend as needed.
• Maintain engineering quality: lead design and code reviews, keep tests and documentation current, and plan delivery with Product, QE and Reliability teams.
• Build agentic systems: design and ship multi-agent workflows with tool use, retrieval and autonomous task execution, with evaluations, guardrails and monitoring.
• Resolve production issues: own investigation and fixes to root cause using logs, metrics and traces.

Requirements:

• 3 to 6 years building and running production systems for consumer-scale products.
• Strong system design: LLD (object-oriented design and design patterns) and HLD (distributed systems, scalability, consistency trade-offs, retries, idempotency, backpressure and circuit breakers).
• Data and messaging depth: data modelling for relational and NoSQL stores, caching strategies, and event-driven design with Kafka or similar.
• Hands-on with analytics systems (ClickHouse, Pinot, BigQuery or similar), or experience building data products.
• REST and gRPC design, API gateways, authentication and authorisation (OAuth, JWT), rate limiting, secrets handling and common security risks.
• Strong in at least one backend language (Java, Python, Go or similar), and comfortable building or changing a web frontend (React or similar).
• Daily use of Claude Code, Cursor or similar tools across building, testing, QA and debugging, and the judgment to know when not to trust the output.
• Experience building agents or multi-agent workflows that real users depend on (for example with LangGraph, Claude Agent SDK or MCP), with evaluations, error handling and monitoring.

Nice to have:

• Cloud and infrastructure experience, especially GCP, containers and Kubernetes.
• Exposure to stream processing with Spark Structured Streaming or Flink.
• LLM platform work: model serving, evaluation pipelines, or tuning cost and latency for LLM workloads.
• Open-source contributions or public technical writing.

Tech stack:

• Languages and frameworks: Java (Spring Boot), Python (FastAPI).
• Databases: Postgres, Redis, Cassandra, ScyllaDB, MongoDB, Bigtable, ClickHouse, Pinot.
• Streaming: event-driven architecture using Kafka, Pulsar or equivalent.
• Cloud and infrastructure: GCP, Kubernetes.
• Experience with these helps, but it isn't required. We hire for fundamentals and expect you to pick up whatever the problem needs.