Final semester student pursuing a Bachelor's Degree in Data Analytics, Business, Finance, Economic, Supply Chain Management, Mathematics or any related field.
Good analytical, problem-solving, and numerical skills.
Bachelor's degree or higher in Computer Science, Information Systems, Data Science, Business Analytics, Statistics, Mathematics, Human Computer Interactive (HCI) or a related field.
Must have 3+ years' experience in MS SSIS as ETL/ data pipeline development platform
Good to have 5+ years of working in data warehouse project implementation or support and maintenance involvement.
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Working TCP/IP fundamentals — can explain what a subnet mask, gateway, DHCP scope, DNS resolution and a default route actually do, and troubleshoot with ping, tracert/traceroute, ipconfig/ip a, and nslookup/dig.
Hands-on configuration of at least a few of: router/firewall, managed switch, VLANs, wireless access points, VPN.
Comfort at a command line — Windows and at least basic Linux, and SSH into a device.
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Working TCP/IP fundamentals — can explain what a subnet mask, gateway, DHCP scope, DNS resolution and a default route actually do, and troubleshoot with ping, tracert/traceroute, ipconfig/ip a, and nslookup/dig.
Hands-on configuration of at least a few of: router/firewall, managed switch, VLANs, wireless access points, VPN.
Comfort at a command line — Windows and at least basic Linux, and SSH into a device.
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Currently pursuing or recently completed a Diploma or Degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence, or a related field.
Basic knowledge of web development, mobile app development, or software engineering principles.
Exposure to AI, machine learning, automation, or AI-assisted coding tools is an advantage.
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Engineering: 10+ years professional software engineering. Strong CS fundamentals. Strong TypeScript/JavaScript and Python. Extensive backend engineering. React/Next.js. Production-scale systems, distributed systems, databases, data-intensive applications, production deployment and operations.
AI / Agentic Systems: strong hands-on experience with several of — Claude/Anthropic, LLM APIs, agentic workflows, tool-using agents, function calling, multi-agent systems, agent memory, RAG, planning systems, reflection loops, evaluation frameworks, autonomous workflows, AI experimentation, LLM observability, prompt optimization, model routing.
We are particularly interested in engineers who have built these systems themselves, not engineers who have only read about them. You should be able to show us production AI systems, agents executing real tasks, complex tool-use workflows, autonomous or semi-autonomous systems, AI evaluation systems, self-improving workflows, and large-scale distributed systems.
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