Embedded AI platform engineer

job description:

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 modern 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