Senior Back End Engineer

Job Title: Senior Backend AI Engineer Location: Fully Remote About Elife Elife is the Enterprise Super App Enabler — the global B2B infrastructure powering rides and instant delivery for the world's largest enterprise platforms. Through API, SDK, AI Agentic, and White-Label integration, Elife connects 100+ enterprise apps — super apps, fintech platforms, OTAs, airlines, map platforms, ride-hailing and delivery apps — to a network of 100+ ride suppliers, 70,000+ local fleets, and 100+ delivery partners across 182 countries. Our bidirectional network is built on a model no single-vendor platform can replicate. The more platforms that join, the stronger the network becomes for every participant: more competitive pricing, faster ETAs, wider global coverage, and new revenue streams that compound across the entire ecosystem. What once required years of effort and billions in capital to build — local operations, supplier relationships, regulatory compliance, dispatch infrastructure — now deploys in weeks via a single integration. Building global infrastructure at this scale demands systems thinkers who move with pace, own end-to-end outcomes, and collaborate seamlessly across markets, cultures and time zones. Our team spans 22 countries — engineers, operators, product creators and partnership leaders across North America, Latin America, Europe, Asia, the Middle East and Africa. We live by one standard: own the outcome, not just the task. We don't compete with super apps.

We power them. Key Responsibilities: Agent Architecture & Pipeline Design Design and operate multi-agent systems with orchestrator and specialist agents covering planning, coding, testing, and review Build feedback loops so agents can detect failures, read error output, and self-correct without human intervention Define agent tool APIs: shell execution, code interpreter, file system access, git operations, CI triggers Implement sandboxed execution environments (Docker, Firecracker, E2B) for safe autonomous code execution Automated Software Delivery Build pipelines where AI autonomously generates unit, integration, and regression tests from specifications Integrate agents with GitHub/GitLab: branch creation, PR lifecycle management, automated review bots Implement CI pipeline agents that interpret test results, triage failures, and propose fixes Automate code review: style checking, correctness analysis, security scanning — all agent-driven LLM & Reasoning Stack Design prompting strategies for code generation, test synthesis, PR narration, and review response Build and maintain RAG pipelines over codebases using vector databases with AST-aware chunking Manage context windows for long-horizon tasks that span multiple files and subsystems Implement fine-tuning and RLHF pipelines to specialize models for domain-specific code generation Evaluation & Quality Define success metrics for agent output before any system ships to production Build and maintain evaluation harnesses that test agent quality systematically across scenarios Benchmark agent performance regressions on each model update or pipeline change Track token costs, latency, and failure rates per agent run through structured observability Safety & Reliability Apply least-privilege execution to every autonomous agent: scope permissions to the minimum required Implement human-in-the-loop gates for destructive or irreversible actions Defend against prompt injection in tool-call pipelines exposed to untrusted content Ensure all agent actions are idempotent — safe to retry without side effects Define and enforce token budgets and cost throttling per agent run Requirements: Non-negotiable experience 5+ years of backend engineering in production systems 2+ years designing or building agentic AI systems (not chatbots or AI autocomplete) At least one production multi-agent system shipped and maintained end-to-end Direct experience with agent output where AI authored the majority of code diffs Hands-on with an LLM evaluation harness for code quality assessment Technical skills Python as primary language; Go or Rust for performance-critical components API design for agent tool endpoints (REST, async queues, event-driven architectures) Kubernetes-based agent orchestration and scaling Vector 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)