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Senior AI Engineer - MSC AI Innovation

Israel, Tel Aviv, Herzliya · Job Posted March 21, 2026
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Job Description

MSC AI Innovation is an AI-first team that incubates, builds, and accelerates solutions aligned to Microsoft most critical business priorities within the Sovereign AI space. We specialize in “0 to 1” work - taking ideas from concept to MVP and later into scalable, production-ready solutions. As a Senior AI Engineer in the MSC IL AI Innovation team, you will lead the design, development, and productization of advanced AI solutions for Microsoft Specialized Clouds, with a strong focus on sovereign-first, secure, and responsible AI systems. You will operate as a technical leader, owning end‑to‑end AI architecture and delivery, influencing platform direction, and mentoring engineers while working closely with architects, product managers, security, and operations teams. This role goes beyond implementation; you will shape how AI is built, governed, and scaled across sovereign and regulated cloud environments.

Job Responsibility

  • Design and build AI agents that plan, use tools/APIs, manage state/memory, and reliably complete multi-step workflows
  • Own AI features from design through production, including deployment, monitoring, and live‑site reliability, with an eval-first development lifecycle: define success criteria, build evaluation datasets and automated harnesses, and run human-in-the-loop reviews where needed
  • Develop and maintain prompt, retrieval, and memory strategies (system prompts, few-shot examples, tool schemas, retrieval context) with proper versioning and evaluation coverage
  • Debug AI behavior using prompt analysis, data inspection, and model/tool-call traces, and translate failure patterns into targeted improvements
  • Establish and track AI quality metrics (e.g., accuracy, groundedness, relevance, hallucination rate) and integrate them into CI/CD release gates
  • Optimize runtime performance and economics (token usage, inference cost, latency, caching, model selection/routing, batching) and implement monitoring and continuous improvement loops (online signals, drift detection, structured user feedback)
  • Partner with product, design, and domain stakeholders to define use cases, acceptance criteria, and rollout plans for AI features
  • Live site responsibility

Requirements

  • 8+ years professional software development
  • 4+ years of software engineering experience in the AI space (e.g., building and shipping AI/ML or GenAI features in production)
  • Proven experience with building AI agents
  • Hands-on experience with evaluation methodologies and integrating quality standards/guardrails into delivery
  • Proficiency in Python and/or C#, with experience using REST APIs and SDKs
  • Deep understanding of AI system design, including ML fundamentals, Generative AI concepts, and cloud-native architectures

Nice to have

  • Bachelor's degree in computer science, Engineering, or equivalent practical experience
  • Strong context engineering and debugging skills across prompts, tool schemas, retrieval pipelines, and model behavior
  • Ability to work effectively with non-deterministic/probabilistic systems and design reliability despite variable outputs
  • Proficiency in software engineering fundamentals (APIs, data structures, CI/CD, observability), applied to AI systems
  • Azure stack: Experience shipping production-grade AI features (LLMs and/or classical ML), on Azure, with measurable quality metrics
  • Experience with LLM observability/tracing and eval tooling, including building internal quality gates and optimizing inference cost/latency in real-time systems
  • Experience with retrieval systems (indexing, chunking strategies, reranking) and grounding techniques
  • Ability to govern AI outputs: define quality standards and guardrails, apply responsible AI practices, and put monitoring/evaluation in place to maintain reliability over time
  • Experience in driving innovation and creating new initiatives from the ground up
  • Proven ability to work independently, own large problem spaces, and collaborate across disciplines
  • Ability to collaborate in a fast‑paced, ambiguous environment and drive clarity across teams

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