Elife Transfer
Website:
elifelimo.com
Company:
https://www.linkedin.com/company/globalelife
Seniority: Mid-Senior level
Industries: Ground Passenger Transportation and Travel Arrangements
Job details:
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)
Click on Apply to know more.