דרושים » הנדסה » IT AI Operations Engineer

Requirements:
What we need to see:
Bachelor's Degree in Computer Science, Engineering, or a related field; or equivalent experience
5+ years of experience
Knowledge of HPC and AI solution technologies from CPUs and GPUs to high speed interconnects and supporting software
Experience with job scheduling workloads and orchestration tools such as Slurm, K8s
Excellent knowledge of Windows and Linux (Redhat/CentOS and Ubuntu) networking (sockets, firewalls, iptables, wireshark, etc.) and internals, ACLs and OS level security protection and common protocols e.g. TCP, DHCP, DNS, etc.
Experience with multiple storage solutions such as Lustre, GPFS, zfs and xfs. Familiarity with newer and emerging storage technologies.
Python programming and bash scripting experience, automation and configuration management tools such as Jenkins, Ansible, Gitops
Knowledge of Networking Protocols like InfiniBand, Ethernet
Experience with virtual systems (for example VMware, Hyper-V, KVM)
Familiarity with cloud computing platforms (e.g. AWS, Azure, Google Cloud)
Ways to stand out from the crowd:
Knowledge of CPU and/or GPU architecture
Knowledge of Kubernetes, container related microservice technologies
Experience with GPU-focused hardware/software (DGX, Cuda)
Background with RDMA (InfiniBand or RoCE) fabrics.

Requirements:
7+ years of experience in building high-quality embedded software using C/C++.
BSc/MSc in Computer Science, Electrical Engineering, or equivalent.
Proven experience developing and maintaining complex embedded systems, including multi-component software stacks, tight HW/SW integration, and system-level debugging.
Proven record of technical leadership in complex embedded systems development, influencing cross-team design or architectural decisions.
Experience in designing and implementing software based on product & hardware specifications.
Experience in data-flow optimization using profiling tools.
Experience working under tight memory, power, and real-time constraints.
Excellent interpersonal and communication skills, with a proven ability to work well in a team.
Advantages:
Interaction with AI compilers / graph optimizers.
Familiarity with fixed-point / quantized inference is a strong plus.
Experience with customer-facing enablement (SDKs, documentation, integration support).
Familiarity with neural network open-source frameworks such as PyTorch and TensorFlow.
Proficiency in Python coding.
Knowledge in computer vision and machine learning.

Requirements:
12+ years in software engineering with strong depth across both development and quality engineering
4+ years as a hands-on principal architect or distinguished engineer with cross-org technical scope
Demonstrated experience designing quality infrastructure used at scale - 50+ engineers, high-velocity pipelines, enterprise SLAs
Direct production experience building or operating systems that incorporate LLMs or AI agents - not evaluations, but shipped systems
Background in large-scale CI/CD architecture and the performance engineering domain
Enterprise SaaS or platform engineering background; familiarity with regulated, high-uptime environments strongly preferred
Agentic AI & LLM Proficiency:
Deep, hands-on understanding of agentic AI patterns: tool use, multi-agent orchestration, planning loops, memory architectures, and human-in-the-loop design
Expert-level prompt engineering including chain-of-thought, few-shot, RAG, and structured output techniques applied to code and test generation
Experience designing evaluation harnesses for non-deterministic AI systems - statistical confidence, behavioral consistency, and regression detection
Familiarity with agent frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or equivalents) and their tradeoffs in production quality pipelines
Technical Depth:
Expert in Python and TypeScript; proficient in at least one compiled language (Java, Go, C#)
Deep knowledge of modern test frameworks across UI, API, and contract layers: Playwright, Stryker, pytest, REST Assured, Pact
Strong CI/CD architecture skills: Jenkins, GitHub Actions, pipeline-as-code, artifact management
Proficient with cloud-native infrastructure: Docker, Kubernetes, AWS, serverless execution environments

Requirements:
3 years of experience in building high-quality embedded software using C/C++.
BSc/MSc in Computer Science, Electrical Engineering, or equivalent.
Proven experience developing and maintaining complex embedded systems, including multi-component software stacks, tight HW/SW integration, and system-level debugging.
Experience in designing and implementing software based on product & hardware specifications.
Experience working under tight memory, power, and real-time constraints.
Excellent interpersonal and communication skills, with a proven ability to work well in a team.
Advantages:
Experience in data-flow optimization using profiling tools.
Interaction with AI compilers / graph optimizers.
Familiarity with fixed-point / quantized inference is a strong plus.
Familiarity with neural network open-source frameworks such as PyTorch and TensorFlow.
Proficiency in Python coding.

Requirements:
12+ years in software engineering with strong depth across both development and quality engineering
4+ years as a hands-on principal architect or distinguished engineer with cross-org technical scope
Demonstrated experience designing quality infrastructure used at scale - 50+ engineers, high-velocity pipelines, enterprise SLAs
Direct production experience building or operating systems that incorporate LLMs or AI agents - not evaluations, but shipped systems
Background in large-scale CI/CD architecture and the performance engineering domain
Enterprise SaaS or platform engineering background; familiarity with regulated, high-uptime environments strongly preferred

Requirements:
3 years of experience in building high-quality embedded software using C/C++.
BSc/MSc in Computer Science, Electrical Engineering, or equivalent.
Proven experience developing and maintaining complex embedded systems, including multi-component software stacks, tight HW/SW integration, and system-level debugging.
Experience in designing and implementing software based on product & hardware specifications.
Experience working under tight memory, power, and real-time constraints.
Excellent interpersonal and communication skills, with a proven ability to work well in a team.
Advantages:
Experience in data-flow optimization using profiling tools.
Interaction with AI compilers / graph optimizers.
Familiarity with fixed-point / quantized inference is a strong plus.
Familiarity with neural network open-source frameworks such as PyTorch and TensorFlow.
Proficiency in Python coding.

Requirements:
B.Sc/M.Sc. in Engineering, Computer Science, or related technical field.
Fluent with Python development
Good communication and organization skills, with a logical approach to problem solving
Good time management, and task prioritization skills
Independent and self-motivated
Advantage:
Experience in C/C++ programming
Please attach an updated grades sheet.

Requirements:
Requirements:
Python Expert: Strong proficiency in modern Python. Deep understanding of asynchronous programming (asyncio) patterns is mandatory, as our entire I/O pipeline (Network, DB, LLM) is non-blocking. Experience with FastAPI and Pydantic (v2).
Agentic Frameworks: Production experience with LangChain. Hands-on experience or deep conceptual understanding of LangGraph (or similar state-machine based agent frameworks).
Deep LLM Expertise (What we mean by "Deep"):
Non-determinism Management: Strategies for handling LLM hallucinations and ensuring reliable outputs (e.g., self-correction loops, specific prompting techniques like CoT/ReAct).
Structured Outputs: Experience forcing LLMs to adhere to strict schemas (Pydantic/JSON mode) for reliable downstream processing.
Context Optimization: Advanced strategies for managing limited context windows (summarization chains, sliding windows, selective context injection) beyond simple truncation.
Inference Economics: Understanding the trade-offs between model size, latency, and cost (e.g., when to route to GPT-4 vs. a smaller/faster model).

Nice to Have:
Experience with Elasticsearch (DSL queries, analyzers).
Knowledge of vector databases and embedding models.
Background in FinTech or familiarity with financial data structures.