דרושים » תוכנה » Senior DevOps Engineer

we are looking for a Senior DevOps Engineer with a strong software development background to build and evolve our internal DevOps platform. You will sit at the intersection of Platform Engineering, SRE, and MLOps - owning the infrastructure and tooling that powers production and enables our developers and data teams to move fast, safely, and with confidence.
If you believe DevOps should be a self-service platform, love automation, and naturally think in systems and end-to-end flows, keep reading.
What You Will Do:
Build a DevOps Platform (Platform-as-a-Product): Create internal services and golden paths that scale across teams (self-service over ad-hoc, repeatable over personal support).
Own CI/CD End-to-End: Design, build, and maintain Jenkins and GitHub Actions pipelines that move code from commit to production with high reliability, visibility, and safety.
Operate and Evolve Our AWS Stack: Hands-on ownership of AWS, including services such as ECS, EKS, Lambda, WorkSpaces, DynamoDB, Redshift, S3, DocumentDB (and the surrounding networking, IAM, observability, and deployment patterns).
Enable MLOps and Data Workflows: Support and automate ML and data pipelines using tools like Airflow, MLflow, and Jupyter Notebooks (plus integrations with compute, storage, and security controls).
Automation First: Eliminate manual work through Infrastructure-as-Code, scripting, and internal tooling. Build reusable components instead of one-off solutions.
Cost Optimization (FinOps): Drive cost visibility and optimization (tagging, budgets/alerts, rightsizing, workload efficiency, and practical trade-offs between cost and reliability).
Security Ownership: Bake security into pipelines and infrastructure (least privilege, secrets management, supply chain controls, vulnerability scanning, hardening, and incident readiness).
Leverage AI to Move Faster: Use AI tools such as Cursor, GitHub Copilot, and Claude Code to accelerate delivery (without compromising quality, security, or reliability).
Cross-Team Collaboration: Partner with Engineering, Data, and AI teams to unblock delivery, improve developer experience, and keep production stable.

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

We are looking for an experienced Senior Backend Engineer to join our Browse Management Core team.

As part of our core team, youll design and implement high-impact backend services, collaborate with frontend, DevOps, and product teams, and play a key role in shaping the next generation of Browse Management architecture.

Youll help build and evolve the backend infrastructure that powers our cloud-native security platform, delivering advanced protection, observability, and scalability at global scale.

If youre passionate about solving complex backend challenges, scaling distributed systems, and driving innovation in cybersecurity, this is the place for you.

Key Responsibilities
Design, develop, and maintain cloud-native backend services supporting a large-scale SaaS security platform.
Build scalable, secure, and resilient architectures using modern AWS services (EKS, ECS, EC2, RDS, SQS, CloudFront, IAM).
Develop and optimize RESTful and event-driven APIs, backend integrations, and data flows.
Work in a microservices-based environment, ensuring high availability, observability, and performance.
Collaborate closely with frontend, DevOps, and product teams to deliver end-to-end solutions.
Implement monitoring, alerting, and reliability best practices for production environments.
Write clean, maintainable, and testable code, contributing to best practices and design standards.
Participate in architecture and design reviews, influencing the technical direction of the platform.

As a Senior MLOps Engineer on the Infra group, youll play a vital role in develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools.
How youll make an impact:
As a Senior MLOps Engineer Engineer, youll bring value by:
Develop, enhance and maintain highly scalable Machine-Learning infrastructures and tools, including CI/CD, monitoring and alerting and more
Have end to end ownership: Design, develop, deploy, measure and maintain our machine learning platform, ensuring high availability, high scalability and efficient resource utilization
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
Work in tandem with the engineering-focused and algorithm-focused teams in order to improve our platform and optimize performance
Optimize machine learning systems to scale and utilize modern compute environments (e.g. distributed clusters, CPU and GPU) and continuously seek potential optimization opportunities.
Build and maintain tools for automation, deployment, monitoring, and operations.
Troubleshoot issues in our development, production and test environments
Influence directly on the way billions of people discover the internet.

we are looking for an AI Engineer to lead our MLOps team and take full ownership of our machine learning infrastructure used to train, evaluate, and deploy our state-of-the-art computer vision models. In this role, you will work closely with other team leads and Product to plan and build the future of core AI platform. You will help define technical direction, make long-term architectural decisions, and translate company goals into executable plans, directly impacting ability to scale its AI capabilities and the overall success of the company.
Responsibilities

Manage and lead the MLOps team, including mentoring, professional development, and day-to-day technical guidance
Plan and own quarterly roadmaps, sprint planning, task breakdown, and execution, ensuring alignment with company and product priorities
Design, build, maintain, and evolve ML infrastructure for training, evaluation, and deployment of computer vision models
Own the end-to-end ML lifecycle in production, including experimentation workflows, training orchestration, model versioning, deployment, and monitoring
Balance delivery and technical excellence by making architectural decisions and enforcing engineering best practices
Work closely with research, data, product, and platform teams to move models reliably from research to production
Actively contribute code to critical components and review design and implementation across the team
Evaluate and introduce new technologies and tooling to improve scalability, reliability, and developer productivity

We are looking for an ML Engineer / MLOps Tech Lead to promote machine learning engineering excellence. Someone who is passionate about building scalable, high-quality data products and processes, while ensuring production systems maintain strong real-time performance observability.
You will focus on designing and maintaining the core infrastructure that empowers the Machine Learning Engineers working within Data Science product teams. Youll collaborate closely with stakeholders across data science, product, and engineering, playing a pivotal role in driving the business by architecting and enabling the infrastructure for machine learning model development, serving, and lifecycle management-the foundation of our product.
Responsibilities:
Partner with MLEs in Data Science product teams and key stakeholders to design and maintain infrastructure for:
Data wrangling - supporting and enabling data requirements for research, training, validation, and testing.
End-to-end ML delivery - enabling model performance development, training, validation, testing, and version control.
Drive engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.
Build and support monitoring and observability tools - dashboards, alerts, and performance tracking of models in production.
Lead architecture projects such as:
Feature Store - centralizing feature engineering and serving across teams.
Vector Databases - enabling large-scale embedding storage and retrieval for advanced ML applications.
GPU Cluster Scaling - optimizing distributed training and inference infrastructure.
Collaborate with product, data science, and engineering teams to solve complex problems, identify trends, and create opportunities through robust ML infrastructure.

Were hiring a ML Engineer to accelerate AI-driven innovation across Stamplis B2B SaaS platform.
Youll be at the forefront of building intelligent systems that power core product experiences and automate internal operations, driving efficiency, speed, and scale across the organization. This is a high-impact, hands-on role in a fast-growing, AI-first company where machine learning is a foundational pillar, not a bolt-on feature. You'll partner with product, engineering, and operations teams to design and implement powerful ML and LLM-based solutions that make a measurable difference.
What You Will Do:
Build Intelligent Systems: Design and develop ML/LLM-powered solutions that solve real-world challenges across Stamplis product and internal workflows.
Own Full Lifecycles: Take projects from concept all the way to production, including model training, evaluation, integration, and monitoring.
Leverage State-of-the-Art Tools: Work with leading frameworks like LangChain, Hugging Face, TensorFlow, and PyTorch to deliver cutting-edge functionality.
Collaborate Cross-Functionally: Partner with product managers, engineers, and stakeholders to embed AI capabilities into user-facing features and backend services.
Ship at Scale: Build and maintain scalable APIs and services, integrating best practices in CI/CD, observability, and cloud infrastructure.
Report with Impact: Share progress, challenges, and results clearly with technical and executive stakeholders.

We are looking for a highly skilled and motivated Senior II Software Engineer to join the Operational Experience engineering team. The team is part of the Customer Experience group, which is responsible for the platform, tools, and customer-facing experiences that power how our customers interact with our ecosystem. This is a high-impact, hands-on role, in which youll be working closely with product managers, designers, customer-facing teams, and engineering partners across the company.

You will operate at the intersection of backend engineering, data-intensive systems, platform development, and customer experience. The ideal candidate brings strong expertise in Node.js and TypeScript, along with deep experience working with large-scale data stores, event-driven pipelines, data models, and high-throughput infrastructure. You will work closely with cross-functional partners to design and implement robust backend services, data-access patterns, and operational workflows that power the portal and internal tools. As we invest heavily in Agentic AI, you will also play a central role in shaping and implementing AI-driven capabilities across the platform. While the role is primarily backend, you will occasionally contribute across the full stack when it supports end-to-end delivery.

If you enjoy owning complex problems end to end, improving systems at scale, and building experiences that bring real value to customers, we would love to meet you.

What you'll be doing:
Drive technical direction and architecture within the OX team and across the broader CX organization. You will proactively identify opportunities to improve performance, resilience, cost, scalability, and developer experience, primarily in backend systems but with influence across the stack.
Lead the development of AI-driven and Agentic AI capabilities. Define how LLMs integrate into our platform, build AI-powered workflows, and establish strong engineering patterns for safe and reliable adoption.
Own and evolve the data foundations behind the portal. Optimize pipelines, improve data quality and freshness, and design resilient data-access patterns across Snowflake, Elasticsearch, Kafka, Redis, MySQL, and related systems.
Work closely with product, design, customer-facing teams, and partner engineering groups. Turn ambiguous problems into clear execution plans and ensure alignment with customer and business goals.
Shape shared standards and platform best practices. Guide other teams on backend services, data integration patterns, portal development approaches, and AI-enabled workflows.
Mentor and elevate engineers across the CX group. Promote engineering excellence, share knowledge openly, and help teams adopt effective modern development practices.
Own delivery of high-impact initiatives. Contribute hands-on when needed, remove blockers, maintain execution momentum, and drive projects from concept to production.

We are looking for a Senior Engineer with a data engineering background to join our growing ML Platform team. This is a great opportunity, whether you have experience with ML and are looking for a ML focused product or are an experienced Data Engineer looking to enter the world of ML. Together well provide tools to develop more effective models, get them into production faster, and ensure that they continue to perform well over time.

What you will be doing:
Designing, building, and maintaining the ML infrastructure that allows our models to make billions of real-time decisions every year.
Building a platform that enables managing a full ML model lifecycle - from researching to training, deploying, and serving predictions in real-time.
Building distributed data processing pipelines to support model development.
Acting as a consultant to researchers, data scientists, and expert analysts and enabling them to research new models faster and with greater precision by providing cutting-edge tooling.
Expanding our ML infrastructure to make it scalable, quick, and efficient to bring diverse models to production and to monitor their performance and drift over time.
Expanding the pool of internal customers able to use ML at Forter. Work with them to understand their needs and help them make the most of the infrastructure that well provide.
Acting as an advocate for MLOps, continually improving our processes, and raising our standards.

We are looking for a Senior DevOps Engineer to join our R&D team in developing the next rising product in the health tech landscape. If you are looking for a challenging, influential position and are passionate about making an impact, this might be the role for you.
As a Senior DevOps Engineer, youll play a key role in the design, development, testing, deployment, and monitoring of our infrastructure and products. In this position, you'll make significant contributions to our observability stack, helping build and maintain robust systems for logs, metrics, traces, and alerting.
Our ideal candidate is passionate about DevOps and observability, has strong communication skills, and thrives on constant improvement for both technology and processes. If you enjoy working on multiple projects in parallel and are a proactive team player, youll fit right in.
This is a unique opportunity to join the core team of a fast-growing startup, where your contributions will have a direct impact on our product and success.
Responsibilities
Support and collaborate with cross-functional engineering teams using cutting-edge technologies.
Contribute to the design, implementation, and maintenance of monitoring, logging, and alerting systems (e.g., Prometheus, Grafana, Loki)
Secure, scale, and manage our cloud environments (AWS and GCP)
Design and implement automation solutions for both development and production
Manage and improve our CI/CD pipelines for fast and safe delivery
Lead best practices in infrastructure, observability, configuration management, and system hardening
Continuously assess and improve existing infrastructure in line with industry standards.