Software Engineer

Overview

Software Engineer on Cisco's Industrial IoT engineering team, building AI-native software for industrial switching, routing, wireless, security, observability, and edge computing.

What you'll do

• Contribute to AI-powered applications for industrial networking, security, observability, and edge computing.
• Build AI applications and agents: develop and evaluate industrial applications and agentic workflows using LLMs, SLMs, machine learning, computer vision, RAG, MCP, and tool calling.
• Apply AI to industrial networking and security: develop capabilities for smarter network operations, troubleshooting, observability, anomaly detection, and threat detection.
• Develop Edge AI: fine-tune, optimise, and deploy ML models and SLMs on edge platforms, balancing model quality, memory, compute, and latency for reliable on-device inference.
• Practice AI-first product development: use coding agents and agentic workflows across design, implementation, debugging, testing, and review.
• Collaborate with domain and AI engineers to deliver reliable software, validating correctness, performance, and security.

What you'll need

• Bachelor's or Master's degree in Computer Science, Computer Engineering, AI/ML, or a related field, with 1–3 years of relevant experience.
• Strong Python skills and proficiency in another language such as C, C++, Go, or Java, with a sound foundation in data structures, algorithms, software design, and debugging.
• Hands-on AI/ML or LLM/agent application development using tools such as PyTorch, TensorFlow, or Hugging Face, including model integration and evaluation.
• Experience using AI coding agents such as Codex, Cursor, Claude Code, or equivalent tools, with the judgment to validate generated code.

Nice to have

• Familiarity with networking domain such as TCP/IP or security protocols experience, or experience in development and validation of peripheral interfaces (e.g. PCI, PCIe, I2C, SPI, UART, GPIO, Ethernet).
• ML/DL and LLM foundations, with exposure to SLM adaptation (fine-tuning, LoRA/PEFT, distillation, quantization) and on-device inference using TensorFlow Lite or llama.cpp under resource constraints.
• Experience with RAG, MCP, or agent orchestration frameworks such as LangGraph; familiarity with A2A is a plus.
• Open-source AI contributions, research, hackathons, or independently developed applications.

Details

• Location: Bangalore, India.