AI Efficiency Architect (Intelligent R&D Productivity Expert)BCSC

Role Overview
You will define and drive the AI-driven efficiency program for software development at BCSC. The core goal is to replace repetitive rule-based daily tasks with AI Agents Skills specifically within Truck ADAS Chassis & Powertrain embedded software development. You will start with the overall productivity architecture identify automatable workflows gradually break business logic into executable Skills and orchestrate Agentic workflows ultimately achieving a step-change in R&D efficiency.

Key Responsibilities
1. Productivity Architecture & Roadmap
Analyze the full software development lifecycle (requirements modeling coding testing integration calibration) for ADAS and Chassis/Powertrain domains. Identify high-effort low-cognitive standardizable daily tasks.
Design a two-tier AI Agent Skill architecture: define Skill granularity I/O standards and fallback mechanisms; design agent decision-making and orchestration logic.
Create a phased roadmap with measurable goals (e.g. 30% reduction in time for test case generation 2x faster calibration data analysis) and visible gains within 36 months.
2. Skillization & Scenario Implementation
Convert typical engineering actions into AI-callable Skills for example:
Automatically parse ADAS scenario requirements generate test cases
Chassis controller change impact analysis auto-update interface docs unit test stubs
Anomaly pattern detection in calibration data produce diagnostic report
Build and maintain a Skill repository to enable reuse and reduce onboarding costs for business teams.
3. Agentic Workflow Development
Orchestrate multi-agent collaboration: e.g. Requirements Change Agent Impact Analysis Agent Auto Test Agent Regression Verification Agent
Enable Agents to call the existing toolchain (Jira Git Matlab/Simulink Vector tools Jenkins etc.)
Design a human-in-the-loop interface: critical decisions require manual approval; routine tasks run fully automatically.
4. Business Alignment & Adoption
Work closely with UAES ADAS Chassis & Powertrain development teams to understand real pain points under AUTOSAR ISO 26262 ASPICE.
Collaborate with functional safety and quality teams to ensure traceability and compliance of AI-generated artifacts (code test cases reports).
Train engineers on using AI productivity tools collect feedback and iterate on Skills.

Qualifications :

Must-Have Experience
5 years in software development or R&D productivity with at least 2 years focused on AI-powered engineering (e.g. code generation auto test generation defect prediction).
Familiar with ADAS or Chassis/Powertrain embedded software development processes. Experience with UAES or similar Tier-1 suppliers is a strong plus.
Proven track record of delivering AI Agent / Copilot / workflow automation projects (beyond just API calls) with measurable efficiency improvements.
Technical Skills
Solid understanding of LLMs (GPT-4 Claude DeepSeek etc.) and their application patterns: prompt engineering RAG function calling multi-agent frameworks (LangChain Semantic Kernel or similar).
Basic to intermediate programming (Python/Java) able to write Skill adapters and orchestration scripts.
Familiarity with at least two embedded development tools: Simulink CANoe Davinci Trace32 etc.
Plus: Knowledge of ASPICE ISO 26262 AUTOSAR architecture.
Core Mindset
Engineering mindset: ability to turn vague efficiency ideas into measurable implementable Skills.
Business-driven: focus on solving real tedious pain points for ADAS/chassis engineers no tech for techs sake.
Persuasive & coaching ability: willing to change team habits backed by data.

Remote Work :

No

Employment Type :

Full-time