Connecting Odds
Capgemini

Senior CAD Semiconductor Engineer

Capgemini
Thessaloniki/Steliou Kazantzid, GRPosted about 2 months agoDiscoveredMatch locked
Full-time

EDA / CAD Flow Expertise

Strong experience with full EDA flows

  • Simulation, synthesis, place & route, verification, DFT flows
  • Tools from Cadence , Synopsys , Siemens EDA (Mentor Graphics)
  • Tool validation, qualification, and deployment

Strong experience with

•

EDA licensing systems (FlexLM/Flexera)

  • Tool versioning, release management, and regression validation

Good understanding of

  • RTL design, verification flows, and physical design constraints
  • Interaction between tools and silicon quality/productivity
  • Knowledge in ISO26262, DO-254 is a plus

Infrastructure & Automation

Strong experience in

  • Linux-based compute farms for EDA workloads
  • Distributed storage systems (NFS, parallel/distributed FS)
  • CI/CD systems (

Jenkins , GitLab CI or equivalent

) Scripting and automation •

Python , Perl , Shell, TCL

  • Infrastructure-as-code and automation mindset

Experience with

  • Monitoring and performance optimization
  • Backup, disaster recovery, high availability

AI / Modern Engineering Tooling

Practical experience with •

AI/LLM-based tooling integration

(e.g., Copilot, OpenAI, Gemini, Mistral)

  • Hybrid AI infrastructure (cloud + on-prem GPU environments)
  • Use cases such as
  • Workflow automation
  • Debug assistance
  • Knowledge retrieval (RAG)
  • Engineering productivity optimization

Additional skills

Object-oriented programming concepts

Familiarity with

  • Version control systems (Git, SVN, etc.)
  • DevOps practices and toolchains

Mission

Define, deploy, and maintain

EDA tool flows and CAD infrastructure

supporting semiconductor design and verification activities across full SoC lifecycle. Drive the evaluation, benchmarking, and integration of EDA tools ( Cadence , Synopsys , Siemens EDA , Arteris ) and associated flows to maximize productivity, reliability, and scalability. Own the

engineering infrastructure environment

(compute, storage, licensing, CI/CD, automation) required for advanced chip design workloads. Collaborate with design, verification, and physical implementation teams to ensure

efficient, robust, and scalable tool usage

. Explore and integrate

AI/ML-based tooling and workflows

to improve engineering productivity (automation, debugging, optimization, knowledge extraction). Ensure optimal trade-offs between

performance, cost, scalability, and usability

of the CAD environment. Act as a

key technical interface

between internal engineering teams, management, and EDA vendors. Provide regular reporting on

tool performance, usage, and roadmap alignment

.

Main Responsibilities

Define, deploy, and maintain

EDA flows

for digital, mixed-signal, and verification activities (simulation, synthesis, P&R, DFT, signoff). Evaluate, benchmark, and qualify

new EDA tools, releases, and methodologies

before production deployment. Design and operate

EDA infrastructure environments

, including

  • Compute farms (Linux-based clusters for simulation and regression)
  • Storage systems (NFS, distributed storage, backup/DR)
  • License servers (FlexLM/Flexera)

Develop and maintain

automation frameworks

for

  • Tool installation and configuration
  • Regression and CI/CD pipelines
  • Resource provisioning and monitoring

Implement and manage CI/CD pipelines (e.g., Jenkins , GitLab CI ) for design and verification workflows.

Collaborate with engineering teams to

  • Optimize tool usage and flows
  • Debug tool-related issues
  • Improve turnaround time and efficiency

Maintain strong interaction with

EDA vendors

( Cadence , Synopsys , Siemens)

  • Tool support, issue resolution, roadmap alignment
  • Licensing strategy and capacity planning

Define and enforce

best practices and standards

for CAD usage and tool flows. Integrate

AI-driven capabilities

into engineering workflows, including

  • LLM-assisted scripting and automation
  • AI-based log analysis, debug assistance, and workflow optimization
  • Hybrid cloud/on-prem AI infrastructure for sensitive data

Support security, compliance, and reliability requirements (e.g., ISO 27001 environments).

Not included in the source posting: benefits.

Skills

artificial-intelligenceci-cdjenkinslinuxllmdevopsgitmachine-learningperlpythonragsoc

Who can apply

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