דרושים » תוכנה » AI Engineer

Requirements:
WHAT YOU WILL BRING
4+ years of professional experience in ML/AI engineering.
Hands-on experience working with LLMs and building AI agents in production environments.
Strong understanding of text classification, ranking, and evaluation in NLP systems.
Proficiency in Python and modern ML frameworks.
Deep knowledge of deploying and maintaining AI systems at scale.
Experience with cloud platforms, particularly AWS.
Experience working independently in fast-paced, mission-driven environments
Bachelors or Masters degree in Computer Science, Data Science, or a related field


NICE TO HAVE
Experience fine-tuning LLMs for domain-specific or task-specific use cases.
Background in cybersecurity applications (e.g., threat detection, incident response, log analysis).
Familiarity with agent-based architectures and autonomous systems.

Requirements:
What You Bring:

5+ years of experience in Data Science or Applied ML roles.

3+ years of backend development experience, working with production systems.

Strong hands-on experience with language models / LLMs and modern ML techniques.

Bachelors degree in Computer Science (or equivalent practical experience).

Proven experience owning end-to-end research POCs, from ideation to real-world impact.

Strong understanding of product and business value, with a bias toward lean, effective solutions.

Deep understanding of language models.


Nice to Haves

Experience in AI security, application security, or threat detection.

Background in LLM workflows, RAG pipelines, prompt analysis, or multi-model systems.

Experience building internal platforms or developer-facing ML tooling.

Previous experience in leadership roles such as Tech Lead or Team Lead.

Startup experience, especially in early or growth-stage companies.

Requirements:
Engineering Foundations:
- Strong Python skills- you write clean, idiomatic, well-tested code and understand the language deeply.
- Hands-on experience using coding agents(Cursor, Claude Code, GitHub Copilot, or similar) to build complex software systems. You know how to delegate effectively to AI assistants and review their output critically.
- Experience with multiple database paradigms- both SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, DynamoDB, or similar). You can choose the right tool for the job.
- Experience designing and integrating with third-party APIs- REST and gRPC. Comfortable building robust clients, handling auth, retries, rate limits, and schema evolution.
- Production experience with Docker and Kubernetes- containerizing services, writing manifests, and debugging deployments.
- Strong Linux fundamentals- confident in bash and the terminal; you can navigate, script, and troubleshoot a server without reaching for a GUI.
- Experience building cloud-native tools on AWS, GCP, or Azure (compute, storage, queues, serverless, IAM).
AI / LLM Expertise:
- Solid understanding of what an LLM is and how it works- tokenization, attention, context windows, sampling, and the practical implications of each for system design.
Strong grasp of modern patterns for integrating LLMs into real workflows, including RAG, MCP (Model Context Protocol), vector databases, agents, tool use, and context engineering- with hands-on experience building with several of them.
- Production experience implementing LLM-powered systems end-to-end, using relevant tools and frameworks (e.g. LangChain, LlamaIndex, LangGraph, Haystack, Pydantic AI, vector stores like Pinecone/Weaviate/pgvector, observability tools like LangSmith or Langfuse).
- Solid foundation in core ML concepts; embeddings, evaluation, overfitting, generalization, and how classical ML relates to and differs from modern LLM-based approaches.
Nice to Have:
- Experience fine-tuning or distilling open-source models.
- Contributions to open-source AI/ML projects.
- Experience with streaming, real-time systems, or low-latency inference.
- Familiarity with prompt evaluation frameworks and LLM-as-judge methodologies.

Requirements:
MSc or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
5+ years of experience in data science or applied machine learning, plus 2+ years in ML engineering or software engineering, with direct responsibility for deploying and maintaining production systems.
Proven ownership of significant AI products or features. You have been a primary technical driver, taking ambiguous problems from initial concept to a working product used by real customers.
Strong foundations in machine learning and statistics, including experimental design, model evaluation, and practical experience with NLP, embeddings, clustering, or related methods for analyzing unstructured data.
Strong Python, SQL and Typescript skills, alongside solid software engineering practices: modular architecture, automated testing, version control, code reviews, and maintainable production code.
Hands-on experience building LLM-powered applications or AI agents beyond the prototype stage, including tool calling, structured outputs, context management, and systematic evaluation
Experience deploying and operating systems in a cloud environment, including containerization, CI/CD pipelines, logging, monitoring, and debugging production issues.
Strong product judgment and independent execution. You can define milestones, prioritize experiments, communicate technical trade-offs, and collaborate effectively across product, engineering, and business teams in a fast-moving environment.
Advantage
Experience as a core technical contributor at a high-growth startup, building new products and scaling them as adoption grows.
Experience in advertising technology, marketing analytics, search, information retrieval, ranking, or recommendation systems.
Familiarity with agent frameworks and SDKs such as ADK, LangChain, or comparable tooling.

Requirements:
Agentic Expertise: Deep experience with LLMs and the modern agentic stack (LangGraph, AutoGPT patterns, tool-calling, and orchestration). You understand how to guide an LLM through complex, multi-step tasks.
The "Full-Stack" DS Mindset: You are a coder first. You are comfortable digging into a large codebase, understanding backend services, and writing production-grade code. You don't wait for someone else to "fix the API."
Product-Driven Research: You are obsessed with impact. You choose the right tool for the job-whether its a simple heuristic or a complex fine-tuned model-based on what provides the most value to the user.
Data & System Fluency: Strong experience with Python and SQL. You understand how to interface with Postgres and ClickHouse to build the data-rich contexts our agents require.
Engineering Rigor: You care about version control, testing, and CI/CD. You treat your prompts and model configurations with the same engineering discipline as code.
The company Mindset: You take ownership, act with accountability, collaborate openly, and focus on delivering meaningful impact. You thrive in fast-moving environments, embrace ambiguity, and enjoy solving hard problems together.
Education: Bachelors or Masters degree in CS, Math, Statistics, or equivalent practical experience in a high-growth AI environment.
Communication: Full professional fluency in both Hebrew and English.

Requirements:
Experience in building and shipping meaningful, production-grade agentic systems - not just demos or prototypes. Were looking for people who can walk us through what they built, the real problem it solved, and the value it created.
Deep, in-depth understanding of LLMs - including how they work under the hood (transformer architecture, tokenization, attention, sampling), their failure modes, and the practical tradeoffs between models, prompting strategies, fine-tuning, and retrieval.
Strong hands-on experience with modern agentic frameworks (e.g., LangGraph, LangChain, or equivalent) and with designing multi-step, tool-using workflows.
Strong command of RAG, prompt engineering, evaluation methodologies, and techniques for improving reliability and reducing hallucinations in LLM-based systems.
Strong proficiency in Python and experience with common AI/ML libraries and APIs (e.g., OpenAI, Anthropic, Hugging Face, PyTorch).
solid software engineering practices, including writing clean, maintainable code and building robust, scalable systems.
5+ years of experience developing machine learning, AI, or software systems.
B.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or similar field.

Requirements:
5+ years of hands-on backend software engineering experience.
B.Sc. in Computer Science or equivalent practical experience.
Proven experience building LLM-powered applications, AI agents, or generative AI systems using modern AI/ML frameworks.
Self-driven and comfortable operating in a fast-paced environment - you don't wait to be told what to do next.
Strong collaborator with clear communication skills; you thrive in diverse, cross-functional teams.
Hands-on experience with multi-agent architectures or agentic evaluation frameworks (preferred).
Familiarity with C# or polyglot engineering environments (a plus).

Requirements:
What we need to see:

B.Sc. (or equivalent experience) in Computer Science, AI, Machine Learning or related field.

8+ years of experience in software development and building production-grade software systems.

2+ years of experience building LLM-based solutions, AI agents, and AI workflows.

Experience working with customers in a technical capacity.

Strong understanding of modern AI/ML concepts and practical applications.

Proficiency in Python.

Excellent people skills with strong communication skills and ability to explain complex technical concepts clearly.



Ways to stand out from the crowd:

Experience with LLMs, RAG systems, or AI agents.

Background in solutions engineering, customer engineering, or technical consulting.

Familiarity with vector databases, embeddings, and evaluation methods.

Ability to quickly prototype and adapt solutions to new use cases.

Strong product thinking and user-centric perspective as well as experience creating demos, POCs, or technical workshops.

Requirements:
Bachelors degree in Computer Science, Engineering or a related field, with focus on AI.
3+ years of hands-on experience in AI/ML engineering, ideally within the fintech industry or a related sector, with a track record of deploying AI agents or agentic workflows in production
Proficiency in Python and SQL for data manipulation, analysis, and AI system building.
Deep experience with LLMs and prompt engineering, including systematic evaluation of enterprise AI solutions
Experience with building RAG systems and Conversational chatbots - Advantage
Experience with cloud platforms (AWS preferred) for deployment and AI-Ops practices.
Analytical Mindset: You bring strong statistical reasoning and business acumen, with the ability to critically assess AI solutions and their real-world impact.
Project Management: You can manage and prioritize multiple tasks, balancing short-term and long-term goals to deliver timely, high-impact results.

Requirements:
Minimum 3 years of professional experience in Data Science roles
Hands-on experience in networking and/or cybersecurity domains
Proven experience building and deploying LLM-based applications and agents (e.g., RAG pipelines, tool-use agents, prompt engineering at scale)
Strong foundation in classical machine learning methods (supervised, unsupervised learning, clustering)
Practical experience with deep learning frameworks such as TensorFlow or PyTorch, including NLP techniques
Proven experience deploying models on cloud platforms like AWS, Azure, or Google Cloud
Excellent analytical, problem-solving, and communication skills
Self-motivated, collaborative, and able to work independently