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Senior Embedded AI Platform Engineers to design, build, and scale AI agents and AI-powered developer tools that transform how embedded software is developed, tested, and shipped.

Resource will work at the intersection of Generative AI, agentic AI systems, and embedded software engineering — building AI solutions that understand the complexity of multi-ECU architectures, real-time operating systems, safety-critical code, and industrial communication protocols.

Must have skills: Embedded C, RTOS, & C++ code understanding, Multi agent development hands on experience in Python; Orchestration experience

What resource will Do

- Understand existing code based of Embedded Systems with RTOS
- Design, build, and deploy multi-agent AI systems that automate software development workflows
- Build context engineering frameworks that enable AI models to produce domain-specific, production-grade output for embedded software
- Architect and implement RAG pipelines, knowledge graphs, and vector database solutions to give AI agents access to large-scale domain knowledge
- Build enterprise integrations that connect AI agents with development tools (GitHub, Azure DevOps, CI/CD pipelines, test management systems)
- Design automated quality gates and validation agents that ensure AI-generated output meets coding standards, safety compliance, and architecture guidelines
- Build observability, metrics, and evaluation frameworks to measure AI impact on productivity, quality, and cost
- Develop full-stack tooling (VS Code extensions, web dashboards, CLI tools) that deliver AI capabilities to engineering teams

What resource can Bring

- Embedded Software Domain Understanding

- Understanding of embedded software development workflows and toolchains
- Familiarity with C/C++ development for embedded systems
- Understanding of testing frameworks and methodologies: GTest, pytest, MIL, SIL, HIL
- Familiarity with real-time operating systems (RTOS) concepts




- Understanding of industrial communication protocols (CAN, J1939, Ethernet)
- Exposure to model-based software development (MATLAB/Simulink) is a plus
- Exposure to QT framework and UI development for embedded displays is a plus

- GenAI & Agentic AI Expertise
- Context engineering — designing and structuring domain context to maximize LLM output quality
- Familiarity with AI-native development tools: GitHub Copilot, Cursor, Windsurf, Antigravity
- LLM-based system architecture (OpenAI, Anthropic, open-source LLMs)
- Multi-agent orchestration and tool-integrated agents
- Retrieval-Augmented Generation (RAG) pipelines
- Vector databases (Pinecone, Weaviate, ChromaDB, pgvector, or equivalent)
- Agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, or equivalent)
- AWS Bedrock, SageMaker, and cloud-agnostic AI architectures
- LLMOps, evaluation frameworks, observability, and guardrails
- Prompt engineering, structured outputs, and function calling
- AI governance, security, and responsible AI design
- Custom & Offline AI Solutions

- On-premise and air-gapped LLM deployments
- Local and embedded AI agents for controlled environments
- Quantized models (GGUF, ONNX) and optimized inference pipelines

- Local LLM orchestration using Ollama, llama.cpp, vLLM
- Fine-tuning, domain adaptation, and hybrid AI architectures
- Full-Stack Development

- TypeScript / JavaScript (Node.js)
- Python
- VS Code extension development or IDE tooling experience
- REST APIs, WebSocket, and up-to-date web application frameworks
- Git, CI/CD pipelines, containerization (Docker, Kubernetes)

Preferred Qualifications

- 5+ years of software engineering experience
- 2+ years of hands-on experience with LLM-based systems, generative AI, or agentic AI
- Experience building AI solutions for engineering or developer productivity use cases
- Experience in regulated or safety-critical industries (automotive, agriculture, aerospace, medical) is a strong plus
- Bachelor's or Master's degree in Computer Science, Software Engineering, AI/ML, or related field

📌 Embedded AI platform engineer (Pune)
🏢 Cyient
📍 Pune