דרושים » ניהול ביניים » דרוש /ה AI Engineering Lead

Requirements:
Knowledge and Experience :
4+ years of backend development experience with strong proficiency in Python.
Proven hands on experience building and shipping GenAI powered features to production.
Strong experience with Python GenAI frameworks such as LangChain, LangGraph, Strands, or similar orchestration frameworks.
Experience integrating LLM APIs into live distributed systems.
Experience implementing structured outputs, validation layers, and guardrails.
Familiarity with evaluation frameworks and LLM quality measurement techniques.
Experience building RESTful APIs.
Strong understanding of clean architecture, scalability, and production best practices.

Advantages:
Experience designing and implementing RAG pipelines.
Experience with MCP servers, A2A architectures, or multi agent systems.
Experience working with embeddings and vector databases.
Proficiency in TypeScript (Node.js or ReactJS).
Experience with AI observability, monitoring, and evaluation tooling.
What We Are Looking For

A strong backend engineer who:
Has integrated LLMs into real production systems.
Understands that prompt engineering is part of a larger distributed architecture.
Knows how to design reliable systems around probabilistic models.
Thinks in terms of validation, monitoring, resilience, and system stability.
Is motivated to deepen expertise in AI system design and orchestration.

Requirements:
12+ years in software engineering with strong depth across both development and quality engineering
4+ years as a hands-on principal architect or distinguished engineer with cross-org technical scope
Demonstrated experience designing quality infrastructure used at scale - 50+ engineers, high-velocity pipelines, enterprise SLAs
Direct production experience building or operating systems that incorporate LLMs or AI agents - not evaluations, but shipped systems
Background in large-scale CI/CD architecture and the performance engineering domain
Enterprise SaaS or platform engineering background; familiarity with regulated, high-uptime environments strongly preferred

Requirements:
12+ years in software engineering with strong depth across both development and quality engineering
4+ years as a hands-on principal architect or distinguished engineer with cross-org technical scope
Demonstrated experience designing quality infrastructure used at scale - 50+ engineers, high-velocity pipelines, enterprise SLAs
Direct production experience building or operating systems that incorporate LLMs or AI agents - not evaluations, but shipped systems
Background in large-scale CI/CD architecture and the performance engineering domain
Enterprise SaaS or platform engineering background; familiarity with regulated, high-uptime environments strongly preferred
Agentic AI & LLM Proficiency:
Deep, hands-on understanding of agentic AI patterns: tool use, multi-agent orchestration, planning loops, memory architectures, and human-in-the-loop design
Expert-level prompt engineering including chain-of-thought, few-shot, RAG, and structured output techniques applied to code and test generation
Experience designing evaluation harnesses for non-deterministic AI systems - statistical confidence, behavioral consistency, and regression detection
Familiarity with agent frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or equivalents) and their tradeoffs in production quality pipelines
Technical Depth:
Expert in Python and TypeScript; proficient in at least one compiled language (Java, Go, C#)
Deep knowledge of modern test frameworks across UI, API, and contract layers: Playwright, Stryker, pytest, REST Assured, Pact
Strong CI/CD architecture skills: Jenkins, GitHub Actions, pipeline-as-code, artifact management
Proficient with cloud-native infrastructure: Docker, Kubernetes, AWS, serverless execution environments

Requirements:
Backend-LLM & agent architecture - 5+ years in production ML/AI and backend systems; recent hands-on experience with backend LLM systems, including agent workflows (e.g., LangGraph or similar), LangChain tooling and chains, state management, and streaming (e.g., SSE). You think in terms of nodes, state schemas, routing, and human-in-the-loop.
Technical stack - Proficient in Python; comfortable with LangGraph, LangChain, FastAPI, PostgreSQL, and optionally Azure AI Search or similar. Experience with LLM providers (OpenAI/Azure, Google Vertex AI, etc.) and RAG (retrievers, chunking, reranking) expected.
Generative AI in production - Proven track record building production GenAI applications, including multi-step agents, RAG, tool-augmented LLMs, and ideally human-in-the-loop or review flows. You care about observability, validation, and safe rollout.
Bachelor's degree or higher in Computer Science or a related field, and strong communication and collaboration skills.

Requirements:
What will you bring to the team?
The "Builder" Attitude: You love creating things. You have experience leveraging automation platforms (n8n, Zapier, Make, Workato) to solve real problems.
Passion for Learning: The AI landscape changes weekly. You are a self-learner who thrives on keeping up with the latest GenAI trends, tools, and techniques.
Structured Thinking (PM Skills): You can manage a project from A to Z. You know how to break down a complex problem into manageable steps, set expectations, and drive execution.
Change Agent: You enjoy helping people adapt to new technologies. You have the communication skills to get teams excited about automation and the patience to guide them through the change.
Tech-Savvy: You understand the logic of data and software (APIs, JSON, Logic Flows) even if you aren't writing code from scratch every day.
Bonus Points (Nice to Have)
Coding Skills: Ability to read/write Python or JavaScript for custom scripts and advanced manipulations.
Advanced Platforms: Experience with enterprise-grade tools like Airflow, Workato, or Retool.
Experience in a Product Management or Technical Project Management role.