Senior Applied AI Engineer

Vibehackers; United States; On-site; USD 140,000 - 170,000; Full time

Benefits offered by this job

Health, dental, and vision plans

401(k) with employer match

Employee assistance program

Job summary

Vibehackers seeks a Senior Applied AI Engineer to design, build, and harden production agentic LLM systems for a regulated insurance platform, integrating agent capabilities with deterministic core. The role shapes AI-first engineering across distributed squads in Vilnius and the US, with architectural influence and product impact.

You will drive multi-agent orchestration, tooling, and RAG pipelines while ensuring reliability, governance, and measurable AI behavior across teams.

Qualifications

• 5+ years of software engineering experience with strong backend or full-stack foundations (APIs, event-driven systems, data modeling).
• Real production experience with LLM-powered systems: agent orchestration, tool calling, RAG, and structured outputs (not just prototypes).
• Demonstrated ability to make agentic systems reliable: evals, fallbacks, deterministic checkpoints, cost and latency management.
• Strong product judgment: apply AI where it adds value and default to deterministic code where appropriate.
• Track record of changing team practices or workflows (introducing harnesses, workflows, or practices that were adopted by others).
• Clear written communication and comfort operating in an async-first, distributed team environment.

Responsibilities

• Design and ship production agentic systems, including multi-agent orchestration, tool use, RAG pipelines, and MCP-based integrations against platform APIs.
• Build reliability layers around agents: evals, guardrails, observability, cost controls, and regression testing to make agent behavior measurable and defensible.
• Implement maker-checker patterns so AI accelerates regulated workflows without autonomously handling money, binding, or compliance decisions.
• Integrate agent capabilities with the deterministic core (canonical data model, Config Hub, Control Tower task orchestration).
• Co-author and implement AI-first engineering practices: agent harnesses, AI-assisted code review, shared skills and prompt libraries, eval-gated CI, and dev-environment standards; drive adoption across squads.
• Shape AI-first product principles: human-in-the-loop patterns, agent UX conventions, and scoping/build-vs-buy decisions informed by AI capability estimates.
• Deliver reference implementations and work with engineering leadership on AI architecture roadmap and production proof points.

Skills

LLM-powered systems

Agent orchestration

Tool calling

RAG pipelines

Distributed teams

Job description

Explicitly focused on production agentic LLM systems (RAG, agent orchestration) and driving AI-first engineering practices across teams.

About the Role

Senior Applied AI Engineer to design, build, and harden production agentic LLM systems for a regulated insurance platform and integrate agent capabilities with a deterministic core. The role also defines and drives AI-first engineering and product principles across distributed squads, with high architectural and organizational influence.

Job Description

Role

Senior Applied AI Engineer responsible for building and hardening production agentic systems for a regulated insurance platform and helping define and implement AI-first engineering and product principles. This is an individual-contributor role with significant architectural and organizational influence, working across distributed squads (Vilnius and the US).

Key Responsibilities

• Design and ship production agentic systems, including multi-agent orchestration, tool use, RAG pipelines, and MCP-based integrations against platform APIs.
• Build reliability layers around agents: evals, guardrails, observability, cost controls, and regression testing to make agent behavior measurable and defensible.
• Implement maker-checker patterns so AI accelerates regulated workflows without autonomously handling money, binding, or compliance decisions.
• Integrate agent capabilities with the deterministic core (canonical data model, Config Hub, Control Tower task orchestration).
• Co-author and implement AI-first engineering practices: agent harnesses, AI-assisted code review, shared skills and prompt libraries, eval-gated CI, and dev-environment standards; drive adoption across squads.
• Shape AI-first product principles: human-in-the-loop patterns, agent UX conventions, and scoping/build-vs-buy decisions informed by AI capability estimates.
• Deliver reference implementations and work with engineering leadership on AI architecture roadmap and production proof points.

Requirements

• 5+ years of software engineering experience with strong backend or full-stack foundations (APIs, event-driven systems, data modeling).
• Real production experience with LLM-powered systems: agent orchestration, tool calling, RAG, and structured outputs (not just prototypes).
• Demonstrated ability to make agentic systems reliable: evals, fallbacks, deterministic checkpoints, cost and latency management.
• Strong product judgment: apply AI where it adds value and default to deterministic code where appropriate.
• Track record of changing team practices or workflows (introducing harnesses, workflows, or practices that were adopted by others).
• Clear written communication and comfort operating in an async-first, distributed team environment.
• Health, dental, and vision plans
• 401(k) program with employer match
• Personal assistance programs (employee support resources)

Compensation

• Compensation Range: $140,000/year - $170,000/year

LLMs Agent Orchestration Tool Calling RAG MCP Config Hub Control Tower Canonical Data Model Prompt Libraries Eval-gated CI Agent Harnesses Maker-Checker Patterns

Skills

System Design Backend Development Full-stack Development API Design Event-driven Architecture Data Modeling Production ML/LLM Systems Agent Orchestration Retrieval-Augmented Generation (RAG) Tool Calling Reliability Engineering Observability Cost Management Regression Testing Product Judgment Architectural Leadership Technical Communication Async Collaboration CI/CD Test Automation Compliance-aware Engineering