Artificial Intelligence & Machine Learning Engineer

We are hiring a Software Engineer in the Artificial Intelligence & Machine Learning job family to join our Enterprise AI Platform team in Israel.

The platform powers AI-assisted engineering workflows across Sandisk and is built on a hybrid on-prem and cloud architecture with agentic orchestration, an MCP ecosystem, an LLM gateway, and memory and knowledge layers. This role focuses on the AI harness: the platform's agentic command-line tooling and the framework of skills, tools, memory, and guardrails that turn a general-purpose model into a governed engineering agent.

As part of the role, you will build distributed software systems and apply them to the runtime, integrations, and evaluation loops that make AI agents reliable inside Sandisk's engineering environment. You will work closely with platform engineers in the US and India and with the firmware, validation, and lab teams in Israel who use the harness every day.

Essential Duties & Responsibilities:
Harness Development: Build and extend the platform's agentic harness, including the CLI runtime, the framework of skills and tool definitions, context and memory management, sandboxing, and execution hooks that govern what an agent is allowed to do.

Distributed Systems: Design and implement the services behind the harness: gateway integrations, session and state management, job execution, caching, and the secure paths that connect developer machines to on-prem platform services.

AI Ecosystem Contribution: Develop MCP servers, skills, and integrations that expose engineering data and tools (Jira, test management, lab benches, internal systems) to agents in a governed way. Contribute reusable components back to the shared platform framework.

Applied AI Understanding: Use working knowledge of how LLM-based systems behave in practice, including tool calling, context windows, prompt and skill design, token cost, failure modes, and evaluation, to make agent workflows predictable and cost-aware.

Reliability & Observability: Instrument harness components with tracing, metrics, and evaluation pipelines. Debug agent behavior end-to-end across the CLI, gateway, and model layers. Participate in incident response and postmortems.

Engineering Quality: Write well-tested, maintainable code. Build automated tests and evaluation suites for agent behavior. Take part in code and design reviews.

Local Partnership: Act as the platform's engineering presence in Israel. Gather requirements from teams adopting the harness, support early users, and feed learnings back into the roadmap.

Continuous Learning: Track advances in agent frameworks, coding agents, MCP, and AI infrastructure, and bring practical improvements into the harness.