דרושים » ניהול ביניים » Principal Solutions Engineer

Requirements:
7+ years of experience in product management, with at least 2 years working on SaaS products.
3+ years of experience working in the application security and/or cloud security vendor space.
Bachelor's or Master's degree in Computer Science, or another relevant engineering or technical field.
A proven track record of delivering customer acquisition growth and optimizing key product metrics throughout the product lifecycle.
Preferred Qualifications
A strong desire to take ownership of new technology products and a drive to design new, innovative, and disruptive solutions.
Inherent passion for cybersecurity and a problem-solving mindset that aligns with challenging the status quo.
Articulate and effective communication skills, both in writing and verbally, with the ability to convey complex ideas to diverse audiences.
Demonstrates a collaborative, 'can-do' approach and works well within a team to achieve shared objectives.

Requirements:
Required Qualifications:
7+ years of hands-on experience in Software Engineering
Experience with modern backend technologies.
B.Sc. or M.Sc. in Computer Science, Software and Computer Engineering, or equivalent military experience
Experience leveraging AI-assisted development tools (Gemini, Claude) to accelerate the software development lifecycle, including code generation, debugging, and unit test creation
Experience in building and managing distributed cloud-based SaaS applications
Deep understanding of cloud-native technologies like Kubernetes, Docker, Microservices architecture, and Public Cloud (AWS/GCP/Azure)
Experienced with monitoring, profiling and enhancing system performance
Excellent written and verbal communication skills
Preferred Qualifications:
Proficiency in Go / Python.
Familiarity with Graph Databases (e.g., Neo4j, Neptune, ArangoDB) is an advantage
Identity Knowledge: Deep understanding of Identity concepts (Users, Groups, Roles, Permissions)
Domain Expertise: Experience in Cybersecurity, specifically in domains like IAM (Identity and Access Management), CIEM (Cloud Infrastructure Entitlement Management), SSPM (SaaS Security Posture Management), or ITDR (Identity threat detection response)

Requirements:
7+ years of hands-on experience in Software Engineering
Experience with modern backend technologies.
B.Sc. or M.Sc. in Computer Science, Software and Computer Engineering, or equivalent military experience
Experience leveraging AI-assisted development tools (Gemini, Claude) to accelerate the software development lifecycle, including code generation, debugging, and unit test creation
Experience in building and managing distributed cloud-based SaaS applications
Deep understanding of cloud-native technologies like Kubernetes, Docker, Microservices architecture, and Public Cloud (AWS/GCP/Azure)
Experienced with monitoring, profiling and enhancing system performance
Excellent written and verbal communication skills
Preferred Qualifications
Proficiency in Go / Python.
Familiarity with Graph Databases (e.g., Neo4j, Neptune, ArangoDB) is an advantage
Identity Knowledge: Deep understanding of Identity concepts (Users, Groups, Roles, Permissions)
Domain Expertise: Experience in Cybersecurity, specifically in domains like IAM (Identity and Access Management), CIEM (Cloud Infrastructure Entitlement Management), SSPM (SaaS Security Posture Management), or ITDR (Identity threat detection response).

Requirements:
Your Experience:
10+ years of progressive experience in DevOps, SRE, Platform, or Infrastructure Engineering roles, with a significant portion at the principal/ tech leadership/ staff, or architectural level.
System Design from Scratch: A proven track record of designing, building, and deploying large-scale, highly available distributed systems and cloud platforms from the ground up.
AI-Powered Automation: Proven experience designing and integrating AI-driven systems, autonomous agents, and LLM-based tools into engineering workflows to optimize development processes, procedures, and overall organizational efficiency.
Communication: Exceptional interpersonal skills, capable of articulating complex architectural and AI workflow concepts clearly to both deeply technical peers and executive leadership.
Cloud & IaC Mastery: Expert-level proficiency with GCP (or equivalent major cloud providers) and deep architectural experience with Terraform.
Advanced Container Orchestration: Deep, internal knowledge of virtualized and containerized environments, with architectural-level expertise in scaling Kubernetes, extending it via custom operators, and automating complex operational logic.
Software Engineering Approach: Advanced coding and automation skills in Python or Go. You treat infrastructure as a software engineering discipline and can build custom tooling/services when off-the-shelf solutions fall short.
Proven Leadership: Demonstrated ability to lead complex, cross-team technical initiatives from conception to delivery, including setting technical roadmaps and driving consensus among stakeholders.
OS/Systems Expertise: Mastery of Linux systems, including kernel tuning, advanced networking, and performance troubleshooting.
Nice to Have:
Deep expertise in managing and scaling stateful workloads and distributed databases (e.g., Cassandra, ScyllaDB, MemSQL, or MySQL) in containerized environments.
Experience contributing to open-source infrastructure projects, or presenting at major tech conferences.

Requirements:
Your Experience
10+ years of progressive experience in DevOps, SRE, Platform, or Infrastructure Engineering roles, with a significant portion at the principal/ tech leadership/ staff, or architectural level.
System Design from Scratch: A proven track record of designing, building, and deploying large-scale, highly available distributed systems and cloud platforms from the ground up.
AI-Powered Automation: Proven experience designing and integrating AI-driven systems, autonomous agents, and LLM-based tools into engineering workflows to optimize development processes, procedures, and overall organizational efficiency.
Communication: Exceptional interpersonal skills, capable of articulating complex architectural and AI workflow concepts clearly to both deeply technical peers and executive leadership.
Cloud & IaC Mastery: Expert-level proficiency with GCP (or equivalent major cloud providers) and deep architectural experience with Terraform.
Advanced Container Orchestration: Deep, internal knowledge of virtualized and containerized environments, with architectural-level expertise in scaling Kubernetes, extending it via custom operators, and automating complex operational logic.
Software Engineering Approach: Advanced coding and automation skills in Python or Go. You treat infrastructure as a software engineering discipline and can build custom tooling/services when off-the-shelf solutions fall short.
Proven Leadership: Demonstrated ability to lead complex, cross-team technical initiatives from conception to delivery, including setting technical roadmaps and driving consensus among stakeholders.
OS/Systems Expertise: Mastery of Linux systems, including kernel tuning, advanced networking, and performance troubleshooting.
Nice to Have:
Deep expertise in managing and scaling stateful workloads and distributed databases (e.g., Cassandra, ScyllaDB, MemSQL, or MySQL) in containerized environments.
Experience contributing to open-source infrastructure projects, or presenting at major tech conferences.

Requirements:
What We're Looking For
We are seeking a strategic technical leader and a chief builder of systems-level designs.
Must-Haves:
Experience: 7+ years of extensive experience in software development, with at least 3+ years in a dedicated architecture or principal engineering role.
Large-Scale System Design: Proven track record of designing and delivering complex, large-scale SaaS platforms or highly distributed systems.
Architectural Expertise: Deep expertise in various system architecture patterns (e.g., microservices, event-driven, serverless) and their practical application.
Cloud Mastery (AWS): Mastery of cloud platforms, especially AWS, including extensive experience architecting solutions using a broad range of AWS services.
Technical Leadership & Governance: Strong capability in technical leadership, architectural governance, and mentorship.
High-Impact Decision Making: Ability to make critical high-level decisions with long-term impact on the system.
Hands-on Technical Expertise: Strong hands-on proficiency in core technologies like TypeScript, Node.js, React, and Python.
Data & Messaging: Deep understanding of high-performance SQL/NoSQL database design and K8S, plus experience with message queue technologies (e.g., Kafka, SQS, RabbitMQ) for scalable, reliable data pipelines.
Communication & Influence: Ability to communicate complex technical concepts clearly and persuasively to diverse audiences, from engineers to executive leadership.
Nice-to-Haves:
Data Tools Experience: Experience within a data tools company (e.g., ETL, Reverse ETL, Observability, Catalog).

Requirements:
Required Qualifications
Core Engineering: 4+ years experience as a Senior ML Engineer, MLOps Engineer, or Backend Platform Engineer (Hands-On) working with cloud environments.
Model Lifecycle Engineering: Hands-on experience managing the technical lifecycle of diverse model architectures, spanning classic ML, LLMs/SLMs, and agentic/RAG systems. This includes engineering scalable data preparation and processing pipelines as well as implementing infrastructure for model training, fine-tuning, optimization, and high-throughput production serving.
Distributed Training & Compute: Strong foundational knowledge of Deep Learning concepts (neural network architectures, training dynamics, optimization techniques) paired with proven experience setting up and optimizing distributed training workloads across multiple GPUs (using PyTorch, DeepSpeed, Megatron-LM, or cloud-native training infrastructure).
Cloud & Infrastructure Architecture: Strong infrastructure knowledge within a major cloud provider ecosystem (GCP, AWS, or Azure), specifically leveraging managed AI platforms and services.
Python Expertise: Expert-level Python skills focused on ML infrastructure, pipelines, and automation frameworks.
CI/CD Integration: Experience with modern CI/CD patterns (such as GitLab CI or GitHub Actions) for automating software and model delivery loops.
AI Tooling & Development: Proficient in leveraging day-to-day AI tools and ecosystems (e.g., Claude, Gemini, MCPs, custom skills, and markdown formatting) to generate, review, and test code dynamically within your development cycle.
Preferred Qualifications:
Strong GCP ecosystem experience.
Background in data science or deep learning workflows.
Cybersecurity domain knowledge.

Requirements:
Required Qualifications
5+ years of hands-on software engineering experience.
Proven experience designing and building large-scale, scalable cloud-based applications.
Expertise in microservices architecture, including technologies like Kubernetes, Docker, GKE, EKS, or AKS.
Experience with relational or NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB) and ORMs (e.g., JPA, Hibernate).
Bachelor of Science in Computer Science or equivalent practical experience.
Preferred Qualifications:
Experience with Java or Kotlin with Spring Boot.
Familiarity with CI/CD pipelines and cloud infrastructure automation.
Experience with big-data architectures and technologies.
Knowledge of cybersecurity, information security, and software security principles.

Requirements:
Required Qualifications
Core Engineering: 4+ years experience as a Senior ML Engineer, MLOps Engineer, or Backend Platform Engineer (Hands-On) working with cloud environments.
Model Lifecycle Engineering: Hands-on experience managing the technical lifecycle of diverse model architectures, spanning classic ML, LLMs/SLMs, and agentic/RAG systems. This includes engineering scalable data preparation and processing pipelines as well as implementing infrastructure for model training, fine-tuning, optimization, and high-throughput production serving.
Distributed Training & Compute: Strong foundational knowledge of Deep Learning concepts (neural network architectures, training dynamics, optimization techniques) paired with proven experience setting up and optimizing distributed training workloads across multiple GPUs (using PyTorch, DeepSpeed, Megatron-LM, or cloud-native training infrastructure).
Cloud & Infrastructure Architecture: Strong infrastructure knowledge within a major cloud provider ecosystem (GCP, AWS, or Azure), specifically leveraging managed AI platforms and services.
Python Expertise: Expert-level Python skills focused on ML infrastructure, pipelines, and automation frameworks.
CI/CD Integration: Experience with modern CI/CD patterns (such as GitLab CI or GitHub Actions) for automating software and model delivery loops.
AI Tooling & Development: Proficient in leveraging day-to-day AI tools and ecosystems (e.g., Claude, Gemini, MCPs, custom skills, and markdown formatting) to generate, review, and test code dynamically within your development cycle.
Preferred Qualifications
Strong GCP ecosystem experience.
Background in data science or deep learning workflows.
Cybersecurity domain knowledge.

Requirements:
Your Experience:
5+ years of software backend engineering experience, including development of scalable server-side applications using Go, Python, or Java.
Practical, hands-on experience with AI, demonstrated by using AI tools in your day-to-day coding workflow, developing AI features for production environments, and/or building personal AI projects.
Strong conceptual understanding of the AI landscape, including various models, frameworks, and prompting techniques.
Demonstrated experience integrating Generative AI solutions into backend architectures.
Proficiency with a variety of database technologies.
Experience with distributed cloud products and major cloud platforms (e.g., GCP, AWS, Azure).
Nice to have:
Experience with containerization and orchestration technologies such as Kubernetes and Docker.
Proven ability to learn and adapt to new technologies and complex technical challenges in a fast-paced environment.