לוח משרות דרושים מהנדס תוכנה עבודה היברידית, לחיפוש זה נמצאו 35 משרות

Requirements:
What you need to succeed in this role:
5+ years of experience in backend development, with a strong track record of building complex production applications in Python - Must.
AI-Assisted Development: Proficiency in utilizing AI development tools to accelerate development workflows, code generation, and problem-solving - Must.
Networking & Security Expertise: Deep understanding of networking protocols (TCP/IP, HTTP/S, SSH, RDP) and security concepts (Zero Trust, Identity Management, Encryption) - Must.
System Architecture: Experience building distributed, high-availability systems that handle real-time traffic and data - Must.
Quick learner and a strong collaborative team player.

Bonus Points:
Experience with Cloud-Native technologies (AWS/Azure/GCP, Docker, K8s).
Experience in the Cybersecurity industry, specifically in Remote Access (VPN/ZTNA) or Identity (IAM).
Familiarity with frontend frameworks like Angular or React.

Requirements:
What You Bring
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related engineering discipline, or equivalent practical experience.
5+ years of professional software engineering experience, including substantial experience building backend systems, distributed systems, or data-intensive applications in production environments.
Strong Python engineering skills, including modular architecture, dependency management, testing practices, observability, and production-grade code quality.
Strong SQL and data modeling expertise, including schema design, indexing strategies, event-driven data models, and scalable analytical aggregations.
Hands-on experience building incremental and idempotent data pipelines that handle duplicate, invalid, and late-arriving events without impacting downstream consumers.
Experience with at least one major cloud platform (Azure, GCP, or AWS) and modern lakehouse technologies such as Databricks, Delta Lake, Spark, or equivalent architectures.
Experience with streaming or messaging technologies such as Kafka, Pub/Sub, Event Hubs, or similar event-driven systems.
Proven ability to diagnose and resolve production data issues, including data quality problems, schema evolution, backfills, replay scenarios, and performance bottlenecks.
Strong written and verbal communication skills in English and experience collaborating effectively with globally distributed teams.

Nice to Have
Experience building industrial, IoT, manufacturing, or operational data platforms.
Familiarity with Digital Twin architectures and industrial data models.
Experience with graph databases, context graphs, knowledge graphs, or relationship-centric data modeling.
Exposure to AI/LLM-powered applications, including retrieval-augmented generation (RAG), agents, tool calling, or evaluation frameworks.
Experience working with Databricks or similar lakehouse platforms from both application and platform perspectives.
Experience building data products that directly support AI agents, intelligent applications, or machine learning workflows.

Requirements:
What You Bring
5+ years of professional software engineering, MLOps, or ML platform engineering experience in production environments.
Significant experience building or owning production ML infrastructure and lifecycle systems.
Strong Python engineering skills with production-grade architecture, modular design, testing, packaging, and robust error handling.
Strong understanding of the end-to-end ML lifecycle including training, deployment, monitoring, retraining, reproducibility, and lineage.
Experience working with large-scale data platforms such as Databricks, Spark, Delta Lake, or equivalent ecosystems.
Experience with ML platform and MLOps frameworks such as MLflow, Metaflow, Kubeflow, or equivalent ML lifecycle-management systems.
Proven ability to design reusable workflow orchestration using Airflow, Metaflow, or Databricks, covering automation, scheduling, dependency management, and production reliability.
Familiarity with operational patterns for LLMOps, AgentOps, and production AI systems.
Strong written and verbal communication skills in English.

Nice to Have
Experience with industrial, IoT or manufacturing platforms.
Experience with feature stores, model registries, dataset versioning, and lineage systems.
Experience with AI agents, RAG systems, production GenAI applications, or evaluation frameworks.

Requirements:
You Will Bring
6+ years of professional software engineering experience.
Strong Python development skills.
Strong backend engineering background, including APIs, services, integrations, or microservices.
Proven experience designing, shipping, or operating LLM-powered applications or GenAI systems.
Experience with RAG, AI agents, tool/function calling, prompt orchestration, evaluation, and observability.
Experience with microservices, distributed systems, and production backend architecture.
Strong understanding of system design, reliability, security, scalability, latency, and maintainability.
Ability to work with complex data models and expose them safely through AI systems.
Ability to operate in ambiguous technical areas and turn prototypes into production-ready systems.
Strong communication skills and ability to explain technical decisions clearly.

Preferred Qualifications
Experience with LangChain, LangGraph, or LangSmith.
Experience with Go, MongoDB, Databricks, Kubernetes, Docker, REST APIs, vector search, or event-driven systems.
Experience with Azure OpenAI, Gemini, or similar LLM platforms.
Experience building or using knowledge graphs, GraphRAG, or graph databases such as Neo4j.
Experience building multi-agent systems or orchestrating multiple specialized agents/tools in production.
Experience building internal platforms, SDKs, developer tools, or shared engineering infrastructure.
Experience with authorization, data segregation, multi-tenant systems, or user-level data scoping.
Experience in industrial, IoT, predictive maintenance, manufacturing, or operational-data domains.

Requirements:
M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field.
4+ years of experience in ML applications, including research on time-series data, ML-based vectoring, and Embeddings (as Transformers, TGNN, TSFM, Graph Transformer, GNN, CNN, etc.)
Proficiency in Python (Pytorch, Pandas, NumPy, PyG - nice to have), and Applied Science SDLC - Discovery, Research, development, fidelity, deployment, monitoring, Training, and fine-tuning
Experience in anomaly detection, abnormal behavior, outliers, pushing recall, precision, and tail metrics KPIs boundaries
Experience working with ML infrastructure such as MLFlow, Vertex AI, Sagemaker etc.
The ability to translate research into scalable, production-ready solutions.
Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning.
Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams.
Experience in feature engineering-based signal processing - a big advantage.

Requirements:
Experience: 3+ years of full-stack development experience, preferably in AI/ML domains
AI Development Tools: Hands-on experience with AI-assisted coding environments, IDEs, and agent-based workflows (e.g., Cursor, Windsurf, GitHub Copilot, Codex, Claude Code, and similar platforms)
Model Knowledge: Deep understanding of different AI model capabilities, limitations, and appropriate use cases (GPT-5, Claude, Gemini, etc.)
Frontend Development: Expertise in React, TypeScript and modern web development practices
Backend Development: Strong proficiency in Java with frameworks like Dropwizard
Database & Infrastructure: Knowledge of scalable database design and containerization
Observability Tools: Experience with tracing, monitoring, and evaluation systems
Collaboration: Strong communication skills for working in a distributed, global team environment
Problem Solving: Ability to work in ambiguous environments and solve complex technical challenges
Nice to have:
Open Source: Experience contributing to or maintaining open-source projects
AI/ML Experience: Understanding of Large Language Models, RAG systems, and AI application architectures
Preferred Qualifications:
Experience with model evaluation, prompt engineering, and LLM optimization
Knowledge of distributed systems and high-throughput data processing
Familiarity with ML experiment tracking and model monitoring platforms
Experience with DevOps practices and CI/CD pipelines
Understanding of AI safety, model security, and responsible AI practices

Requirements:
MSc in Computer Science (or related field) from a top-tier institution - Big Advantage

3+ years of software engineering experience, with strong proficiency in Python (primary) and familiarity with Java / C++ / JavaScript codebases.

Hands-on experience with LLMs / GenAI systems, including prompt engineering, inference optimization, and model evaluation.

Experience designing LLM-driven pipelines (agentic or tool-augmented), including:

Structured outputs (JSON schemas), Multi-step reasoning/verification flows

Excellent communication and collaboration skills - Must

Advantage:

Familiarity with ML for code, static/dynamic analysis tools, and AST-level representations

Experience with web application security, vulnerability detection, or remediation

Proficiency in one or more of: JavaScript, C#, Python, Go, PHP

Familiarity with cloud-native environments and CI/CD workflows (AWS, Azure)

Requirements:
Team player at heart.
4+ years of experience in FinOps, Cloud Cost Management, Infrastructure Operations, DevOps or a similar role.
3+ years of experience in software development (backend/data)
Hands-on experience managing and optimizing cloud spend in AWS or other major cloud providers.
Ability to translate cost insights into practical technical engineering solutions.
Strong understanding of cloud billing, tagging, cost allocation, budgets, alerts, reservations, savings plans, and usage-based cost models.
Ability to understand architecture and assess the effort and impact of cost-optimization initiatives on performance, reliability, and business outcomes.
Experience building dashboards, reports, COGS forecasts, and recurring cost-review processes for engineering and leadership teams.
Ability to analyze large datasets, identify cost drivers, and translate findings into clear recommendations.
Strong collaboration skills and the ability to work with Engineering, Finance, Product, and leadership stakeholders.
Ability to drive cost-optimization initiatives from discovery through execution and measurable impact.
Strong ownership, independence, and attention to detail.
Excellent communication skills and the ability to explain technical and financial tradeoffs clearly.
Advantages:
B.Sc / M.Sc in Computer Science / Software Engineering
Experience working in a SaaS company with production-scale cloud infrastructure.
Experience working with Datadog and Snowflake.
Experience with AI/ML infrastructure costs, data pipelines, or large-scale compute environments.
Experience working in a regulated environment such as healthcare, fintech, or enterprise SaaS.

Requirements:
Team player at heart.
Hands-on full-stack Experience in developing a large-scale system
5+ years of experience as a Software Engineer
3+ years of experience with client-side frameworks (preferably React)
3+ years of experience with server-side frameworks - Python/ Node.js
Experience with AWS or other cloud services
Advantages:
Experience developing with React Native
B.Sc / M.Sc in Computer Science / Software Engineering
Familiarity with Git, CI/CD framework and testing
Experience with python frameworks
Experience with serverless architecture
Knowledge of UI/UX principles and experience with design systems
Experience with RDS / Snowflake

Requirements:
Bachelors degree in Computer Engineering, Electronics, or Electrical Engineering.
Masters or PhD in a relevant domain is highly desirable.
10+ years of experience in product R&D within the computer/system software industry, focusing on kernel and driver development.
Knowledge and Skills:
In-depth working knowledge and Hands-on experience in I/O subsystems, programming, and kernel development.
Expertise in C and C++ programming languages.
Strong understanding of operating system internals and kernel & driver internals, including memory management, threading, and resource scheduling.
Proven expertise in debugging, kernel triaging, and resolving memory and performance-related issues.
Proficient in performance profiling, multithreading, and driver programming.
Strong Knowledge in Linux and virtualization technologies such as VMware, Hyper-v and KVM.
Ability to Design and implement scalable, efficient solutions for complex datapath and kernel challenges.
Strong ability to innovate and bring fresh approaches to technical challenges.
Excellent problem-solving skills with a proactive mindset and sharp analytical abilities.
Exceptional communication skills to effectively present and discuss technical designs, proposals, and solutions with senior management and stakeholders.
Demonstrated technical leadership with a focus on driving innovation.

Requirements:
Bachelor's or masters degree in computer science, engineering, information systems, or closely related quantitative discipline.
Typically, 10-15 years experience.
Native in Hebrew and fluent in english
Knowledge and Skills:
Strong programming skills in Python, Java, Golang, or JavaScript.
Deep expertise in software systems design, development methodologies, and integration across diverse platforms and technologies.
Proficient in cloud-based security concepts like using identity and access management, firewalls, VPN, and in-plane switching systems.
History of innovation with multiple patents or deployed solutions in software design.
Excellent written and verbal communication skills.
Ability to effectively communicate product architectures, design proposals, and negotiate options at business unit and executive levels.

Requirements:
7+ years of experience developing and operating large-scale, high-availability systems.

7+ years of experience with Python (Experience with Go is a plus).

Experience working with cloud environments (AWS preferred) and big data technologies (Spark, Airflow, S3, Snowflake, EMR).

Familiarity with metrics systems (e.g., Prometheus, cloud monitoring APIs) or time-series data - a strong plus.

Autodidact, self-motivated team player with strong communication skills and a passion for solving challenges at scale.

Requirements:
5+ years of experience in software engineering.

Proficiency in NodeJS and React (or deep JavaScript expertise).

Strong understanding of both frontend and backend systems.

Autodidact and self-driven with a problem-solving mindset.

Excellent communication and collaboration skills.

Comfortable in a fast-paced, startup environment.

Academic degree in Computer Science (or similar).

Familiarity with CI/CD pipelines and modern DevOps practices is a plus.

Requirements:
Requirements:

7+ years of hands-on experience in software engineering, with at least 1-2 years working on generative AI projects (LLMs, diffusion models, multimodal models, etc.).

Proven ability to go from idea to production-ideally with examples of real-world AI features youve shipped.

Fluency in Python, Node.js, or similar languages used in ML and full-stack development.

Experience with prompt engineering, fine-tuning, or embedding models using frameworks like LangChain, LlamaIndex, or similar.

Familiarity with AWS services and best practices, including Lambda, S3, SageMaker, ECS/EKS, Bedrock etc.

Experience with MLOps and model deployment practices (e.g., containerization, GPU inference, vector databases).

Creativity and initiative-able to pitch and prototype ideas with minimal oversight.

Strong communication skills and the ability to explain technical concepts to non-technical stakeholders.


Nice-to-Haves:

Prior experience integrating generative AI in FinOps or cloud cost optimization tools.

Background in NLP, computer vision, or other relevant ML fields.

Contributions to open-source AI tools or research.

Knowledge of responsible AI principles and handling model risks.

Requirements:
At least 5 years of hands-on programming experience - Must.
BSc. in Computer Science, Computer Engineering, Mathematics, or any relevant scientific field (advanced degrees are an advantage) - Must.
Experience in computer architecture, assembly language, and low-level programming concepts - Advantage.
Experience working in a multidisciplinary environment - Advantage.
Familiarity with MLIR/LLVM - Advantage.
A motivated and resourceful problem solver with a passion for tackling complex technical challenges, especially in hardware-oriented environments.