Senior Back End Engineer

Job Title: Senior Backend AI EngineerLocation: Fully RemoteCompany OverviewFounded in 2019, Elife Transfer is a fast-growing startup headquartered in San Francisco, California, at the heart of Silicon Valley. We are an all-in-one global ground transportation marketplace, enabling travelers to book airport transfers, ride-hailing, shared rides, private cars and rail tickets. Trusted by over 40 million travelers across 182+ countries, we are rapidly scaling to become the world’s go-to platform for seamless, end-to-end ground mobility.Key Responsibilities:Agent Architecture & Pipeline DesignDesign and operate multi-agent systems with orchestrator and specialist agents covering planning, coding, testing, and reviewBuild feedback loops so agents can detect failures, read error output, and self-correct without human interventionDefine agent tool APIs: shell execution, code interpreter, file system access, git operations, CI triggersImplement sandboxed execution environments (Docker, Firecracker, E2B) for safe autonomous code executionAutomated Software DeliveryBuild pipelines where AI autonomously generates unit, integration, and regression tests from specificationsIntegrate agents with GitHub/GitLab: branch creation, PR lifecycle management, automated review botsImplement CI pipeline agents that interpret test results, triage failures, and propose fixesAutomate code review: style checking, correctness analysis, security scanning — all agent-drivenLLM & Reasoning StackDesign prompting strategies for code generation, test synthesis, PR narration, and review responseBuild and maintain RAG pipelines over codebases using vector databases with AST-aware chunkingManage context windows for long-horizon tasks that span multiple files and subsystemsImplement fine-tuning and RLHF pipelines to specialize models for domain-specific code generationEvaluation & QualityDefine success metrics for agent output before any system ships to productionBuild and maintain evaluation harnesses that test agent quality systematically across scenariosBenchmark agent performance regressions on each model update or pipeline changeTrack token costs, latency, and failure rates per agent run through structured observabilitySafety & ReliabilityApply least-privilege execution to every autonomous agent: scope permissions to the minimum requiredImplement human-in-the-loop gates for destructive or irreversible actionsDefend against prompt injection in tool-call pipelines exposed to untrusted contentEnsure all agent actions are idempotent — safe to retry without side effectsDefine and enforce token budgets and cost throttling per agent runRequirements:Non-negotiable experience5+ years of backend engineering in production systems2+ years designing or building agentic AI systems (not chatbots or AI autocomplete)At least one production multi-agent system shipped and maintained end-to-endDirect experience with agent output where AI authored the majority of code diffsHands-on with an LLM evaluation harness for code quality assessmentTechnical skillsPython as primary language; Go or Rust for performance-critical componentsAPI design for agent tool endpoints (REST, async queues, event-driven architectures)Kubernetes-based agent orchestration and scalingVector databases and embedding pipelines (Weaviate, Qdrant, Pinecone)Distributed tracing and observability tooling (OpenTelemetry, Datadog, LangSmith)Nice to have • Experience with SWE-bench, HumanEval, or other code generation benchmarks • Contributions to open-source agentic frameworks • Knowledge of formal verification or property-based testing for agent output validation • Experience deploying LLMs on custom hardware (A100/H100 clusters) • Background in compiler design or static analysis (useful for AST-level code understanding)

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