Staff Backend Engineer

As a Staff Engineer - Backend, you will be a technical anchor across one or more product domains. You will design distributed systems, embed GenAI capabilities into production backends, mentor engineers, and influence architecture decisions at the org level. This is a hands-on leadership role where you will be expected to write production-grade code every week while simultaneously driving technical strategy.

The candidate will have responsibilities across the following functions:

Applied GenAI and AI/ML:

• Architect and ship production GenAI features using LLMs (OpenAI, Azure OpenAI, Gemini, Claude) integrated into Java/Spring Boot backends
• Design and own RAG pipelines, chunking strategies, embedding models, vector store selection (Pinecone, pgvector, Weaviate), retrieval optimisation
• Build LLM orchestration layers using LangChain4j, Spring AI, or custom agentic frameworks
• Implement AI agent workflows with multi-step reasoning, tool use, memory, and guardrails in production
• Evaluate and fine-tune prompt engineering strategies; move beyond basic prompting to structured output, function calling, and chain-of-thought

Hands-On Coding and Code Quality:

• Write production-grade Java 17/21 code weekly; active authorship of complex backend modules
• Build with Spring Boot 3 x, including reactive patterns (WebFlux), virtual threads (Project Loom)
• Champion Testcontainers-based integration testing, contract testing, and mutation testing frameworks
• Enforce code quality through PR reviews, pair programming, and architectural guardrails

System Design and Architecture:

• Lead design of large-scale distributed systems and event-driven architectures using Apache Kafka
• Apply hexagonal architecture (ports & adapters), DDD, and CQRS/Event Sourcing patterns
• Architect multi-tenant SaaS backends with isolation, scalability, and performance as first-class concerns.
• Design data models across MongoDB, PostgreSQL, and Redis at scale.
• Define and govern API contracts: REST, gRPC, AsyncAPI across multiple consuming teams.

Software Engineering Fundamentals:

• Deep expertise in data structures, algorithms, concurrency, and JVM internals.
• Strong command of distributed systems theory: CAP theorem, eventual consistency, idempotency, saga patterns.
• Proficiency in Docker, Kubernetes, and AWS (ECS/EKS, Lambda, S3 SQS, RDS).
• Experience with CI/CD pipelines, GitOps, blue-green deployments, canary releases, and feature flags.

Tech Lead & Influence Without Authority:

• Serve as technical north star for a squad of 6-10 engineers, setting standards, reviewing architecture, and unblocking decisions.
• Drive RFC processes for significant technical changes, written, structured, cross-team.
• Mentor SE II and Senior Engineers through structured technical coaching and design review facilitation.
• Own technical roadmap contributions; advocate for platform investments and tech debt reduction.

Product and Business Thinking:

• Translate ambiguous product requirements into well-scoped technical solutions with clear trade-off documentation.
• Proactively identify technical risks that impact business outcomes: latency, reliability, cost, and compliance.
• Contribute to build vs. buy decisions and vendor evaluations with structured analysis.

Responsible AI and Observability:

• Implement LLM observability prompt/response logging, token usage tracking, latency dashboards, hallucination detection.
• Apply AI safety and guardrails output filtering, PII redaction, rate limiting, and fallback handling.
• Design systems with OpenTelemetry, Prometheus, Grafana, and ELK/Datadog for full-stack observability.

Requirements:

• 10-14 years of backend engineering with Java as the primary language.
• Hands-on production GenAI experience: RAG, LLM APIs, agent frameworks (not just exploration or POCs).
• Expert-level Spring Boot 3 and microservices architecture.
• Deep Apache Kafka experience: design, operations, and tuning.
• Strong MongoDB and/or PostgreSQL data modelling at scale.
• Proven tech lead experience mentoring, architecture ownership, cross-team influence.
• AWS cloud proficiency with at least 3+ core services in production.
• Excellent written and verbal communication; can write RFCs and present to senior leadership.
• Experience with LangChain4j, Spring AI, or LlamaIndex for Java-based AI orchestration.
• Contributions to open-source projects or published technical writing.
• Exposure to vector databases (Pinecone, pgvector, Qdrant, Weaviate).
• Background in automotive, fleet, or dealer tech platforms.
• Certifications: AWS Solutions Architect, GCP Professional Cloud Architect, or equivalent.