דרושים » הנדסה » ML Engineer

Requirements:
LLM experience: LangChain, vLLM, HuggingFace toolkit
Machine Learning oriented languages, tools, and frameworks: Spark, Python
Cloud technologies, in particular AWS, and Software container technology: Docker, Kubernetes, KubeFlow / MLflow
Experience with designing and developing Generative AI architectures
Machine learning techniques (classification, regression, and clustering) and principles (training, validation, and testing)
Data query and data processing tools or systems: relational, NoSQL, stream processing
Distributed computing systems and related technologies: Spark, Hive
Software engineering fundamentals: version control systems (Git, Github) and workflows, and ability to write production-ready code
Computer science fundamentals: data structures, algorithms, performance, complexity, and implications of computer architecture on software performance (I/O and memory tuning)
Mathematics fundamentals: linear algebra, calculus, probability
BS, MS, or PhD degree in Computer Science or related field, or equivalent practical.

Requirements:
To succeed in this role, youll need:
Bachelors / Master's degree or PhD in Computer Science or a related technical field, specialisation in Security or AI/ML or an exceptional, well-evidenced track record substituting for it
10+ years of software engineering experience, including a track record of owning the architecture of complex systems at enterprise scale
A track record of setting technical direction beyond your own team - architecture or standards other teams adopted, senior engineers you've levelled up, and comfort operating where the problem isn't yet defined
7+ years in security engineering - network and systems security, security protocols, design reviews, and threat modeling - with a focus on AI-specific work in recent years
Multiple demonstrated engagements threat modeling and/or securing LLM, GenAI, or agentic systems, with evidence of platform-level or cross-team impact
Personal ownership of prompt injection / jailbreak defense, guardrail architecture, or AI red-teaming programs - ideally having built the program/methodology, not just executed within one
Deep, applied experience securing model, RAG, or data pipelines, including having designed the access-control and data-governance model others build against
Fluency with the OWASP LLM Top 10 (or equivalent) to the point of extending, critiquing, or contributing to such frameworks - not just applying them
Deep hands-on experience with agentic AI frameworks (e.g., LangChain, LangGraph)
Clear communication, a collaborative default, and a bias toward learning fast in a field that changes constantly
Bonus
Strong command of auth protocols (OAuth 2.0, OIDC, PKCE, API Keys) and agentic protocols (MCP, A2A), including having designed identity/consent models for them
Built and open-sourced (or productized) security tooling/automation for AI systems now used beyond one team
Track record of CVEs, top-tier publications, or invited talks specifically in AI/ML or LLM security
Prior experience setting AI security strategy at a platform or company level, not just a single product
5+ years of programming experience in Java or Python.

Requirements:
4+ years of experience in a DevOps or related engineering role, with a strong background in AWS and cloud-native environments. Proven ability to design, manage, and maintain high-scale production systems, ensuring reliability, performance, and scalability.
Deep expertise in cloud technologies and application security, with a strong focus on best practices for securing resilient, scalable, and cost-efficient architectures.
Extensive experience with containerization and orchestration technologies, including Docker, Kubernetes, and Helm.
Proficiency in automation tools such as Terraform, and hands-on experience with CI/CD pipelines using tools like Jenkins.
Strong knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, Loki) to ensure system health, optimize performance, and proactively detect issues.
Proficiency in scripting languages (e.g., Node.js, Bash) to automate workflows and enhance operational efficiency.
Excellent communication, collaboration, and documentation skills, with a proactive approach to problem-solving.
Passionate about learning new technologies and tackling complex challenges in a fast-paced environment.
Hands-on experience managing big data infrastructure, optimizing performance and scalability for data-intensive applications.