Requirements:
Managerial experience - at least 2 years as R&D team lead
Python experience
Experience with public cloud development
Designing data models for both NoSQL and relational databases
Experience in building scalable RESTful APIs and using external/internal third-party APIs
Experience with software engineering best practices (e.g. unit testing, code reviews, design documentation, KISS!)
Familiarity with at least one server-side framework (e.g., FastAPI, flask, Spring, Django, Rails, Express, ASP.NET )
Advantages:
Experience with k8s
Experience with Agile methodology
Experience with Domain Driven Design, Event-Driven architecture, and Microservices patterns and tooling
system architecture and design skills of large scale web services
frontend / Full Stack exp.

Requirements:
.Sc. in Electrical Engineering or related field
5-10 years of experience in FPGA RTL design and verification
Strong background in fixed-point algorithm implementation
Experience with high-speed interfaces (PCIe, JESD204, Ethernet)
Familiarity with Xilinx platforms (Zynq, MPSoC, RFSoC) - advantage
Basic programming skills ( C / C ++, Python )
Experience in PCB bring-up and lab testing - a plus
Strong teamwork and communication skills

Requirements:
BSc degree in Computer Science or Engineering
" 5+ years of software development experience in networking or systems programming
" Strong proficiency in C / C ++ and Python programming
" Understanding of networking protocols and packet processing optimization
" Strong debugging skills with networking and performance analysis tools
Preferred Qualifications
" Experience with SONiC development, SAI implementation, or switch software
" Knowledge of Linux Kernel networking
" Experience with smart NIC programming interfaces and SDK development
" Familiarity with DPDK, RDMA, and high-performance networking libraries
" Experience with AI/ML framework networking integration

Requirements:
- 3+ years of hands-on experience as a data Scientist.
- BSc or MSc in a relevant field such as data Science, Mathematics,  Computer Science, Statistics.
- Extensive hands-on work with time-series datasets and modeling techniques.
- Proficiency in Python and common data science libraries (pandas, NumPy, scikit-learn).
- Experience using  Machine Learning frameworks and knowledge of its fundamentals (eg feature selection, bias tradeoff)