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Tiebreak · Tel Aviv District, Israel

Senior AI Engineer — Agent Platform & Evaluation

seniorfull timePosted 2 days ago
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Stack mentioned

agentic-aikubernetespythonanthropicawsllmopentelemetryprometheusgrafanapostgresqlazuredevops

About Tiebreak Solutions

Tiebreak is a global software and infrastructure company behind a high-performance trading platform and a marketing/CRM platform. We're in the middle of a significant transition — consolidating and modernizing our system(s), and this is a unique opportunity to genuinely influence how it gets built.

We are hiring an AI engineer — Sofia or Israel.

We have AI agents doing real work against production codebases today: they take a work item, plan it, write the code, review it, and open a pull request for a human to approve. What that system does not have is anyone whose job is to make it measurable.

Responsibilities:

- Building the agents — new ones, and extending the ones already running.

- Their tools, and the context architecture that decides how each agent behaves.

- The evaluation harness that catches an agent getting worse before the change merges.

- The telemetry that proves what any of it is actually worth.

- Budgets that govern model spend instead of observing it.

- The retrieval layer that lets an agent find what it needs instead of reading an entire repository to answer one question.

- The runtime underneath is moving from self-managed Kubernetes to Bedrock AgentCore, you will own the second half of that migration.

Requirements/Tools:

Experience with the following:

- Python – used across the service and agent codebases.

- Temporal - used for orchestration — multi-stage pipelines that run for hours with human approval gates in the middle.

- LangGraph and Claude Code (agent runtimes).

- AWS Bedrock - for models behind a LiteLLM gateway that keeps provider choice open.

- Langfuse – used for LLM traces and evaluation.

- OpenTelemetry with Prometheus and Grafana for everything else.

- PostgreSQL with pgvector used for memory and caching.

- MCP servers for tools.

- Azure DevOps Repos and Pipelines for delivery.

- Experience in startup environments would be considered an advantage