Senior AI Engineer - Agentic Systems

Gloat • יפו, TA, IL

Actively Hiring

Full-time Posted 5 days ago

Responsibilities

check_circle Design and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
check_circle Implement retrieval and RAG pipelines over structured and unstructured workforce data, grounded in our knowledge graph connecting people, jobs, and skills.
check_circle Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
check_circle Build evaluation harnesses, guardrails, and safety/bias checks, and work within the governance engine so agents behave reliably, respect customer policies, and produce a full audit trail.
check_circle Ship agents in a model-agnostic way across providers (Anthropic, Google, IBM watsonx) and deploy them into Teams, Slack, and Copilot.
check_circle Optimize agents for latency, cost, and reliability at enterprise scale.
check_circle Take agents from prototype to production — with monitoring, observability, and a fast iteration loop.
check_circle Partner closely with product, data, and platform teams to translate customer needs into agent capabilities.

5+ years building production software
Proven experience building and shipping LLM agents to production — not just demos or prototypes.
Hands-on with at least one agent orchestration framework (e.g. LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar).
Strong Python and solid software engineering fundamentals.
Prompt engineering paired with systematic, measurable evaluation of LLM outputs.
Experience with tool use / function calling and integrating LLMs with external systems.
Track record deploying, monitoring, and maintaining AI in production (cloud, CI/CD, observability).

2+ years hands-on with LLMs / generative AI.
Practical experience with RAG, embeddings, and vector databases (e.g. pgvector, Pinecone, or similar)
Experience with MCP, agent memory, and planning/reasoning patterns.
Background in HR tech, people data, or skills ontologies.
Knowledge graph / graph ML experience (knowledge graphs, GNNs).
Responsible AI: bias evaluation, guardrails, and AI governance.
Experience working across multiple model providers (e.g. Anthropic, Google, IBM watsonx) rather than a single vendor.

Fulltime Ai Ai Engineer

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