Role Overview

We are building an AI-first semiconductor company, where AI is deeply embedded into every aspect of engineering—from architecture and RTL design to verification, physical design, and operations.

We are looking for a highly capable AI Engineer / AI Platform Architect who will define and drive our AI strategy, infrastructure, and agent-based workflows for semiconductor design. This role sits at the intersection of AI, EDA, and engineering productivity, and will be instrumental in transforming how chips are built.

What You’ll Do

AI Strategy & Vision

- Define and execute the AI roadmap for semiconductor design workflows across:

- Architecture

- RTL design

- Verification

- Physical design

- Analog design

- Identify high-impact opportunities where AI can significantly improve:

- Productivity

- Quality

- Time-to-silicon

- Serve as the central thought leader for AI adoption across the company

AI Infrastructure & Platform

- Architect and deploy AI infrastructure, including:

- Cloud-based (e.g., AWS) and/or on-prem (air-gapped) environments

- GPU/compute resource planning and scaling

- Define strategy for:

- Model hosting vs API usage

- Offline/private model deployment for IP-sensitive environments

- Build systems for:

- Data management, protection, and governance

- IP security and compliance

- Auditability and traceability of AI-generated outputs

AI Agents & Workflow Automation

- Work closely with engineering teams to:

- Identify workflows suitable for AI agent automation

- Define multi-step agent pipelines spanning different tools and domains

- Design and implement AI agents that can:

- Interact with EDA tools

- Execute multi-stage workflows (e.g., generate ��� simulate → analyze → refine)





- Integrate across RTL, DV, and physical design flows

- Build reusable agent frameworks and orchestration layers

AI Guardrails & Governance

- Define and enforce AI guardrails, including:

- Safe usage policies

- Data privacy and IP protection

- Model access controls

- Manage:

- Token usage and cost optimization

- Access policies for different teams

- Ensure AI usage aligns with enterprise-grade security standards

LLM & Tooling Expertise

- Evaluate and recommend LLMs and AI tools for different use cases:

- Code generation

- Debugging

- Documentation

- Data analysis

- Continuously benchmark and optimize model selection across:

- Performance

- Cost

- Privacy constraints

- Stay current with advancements in:

- LLMs

- Agent frameworks

- AI tooling ecosystem

Enablement & Training

- Train engineering teams to:

- Effectively use AI tools and agents

- Build their own custom AI agents

- Apply prompt engineering best practices

- Create documentation, playbooks, and templates for:

- AI-assisted workflows

- Agent development

- Drive a culture of AI-native engineering

What We’re Looking For

- Bachelor’s/Master’s/PhD in Computer Science, Electrical Engineering, or related field

- 8+ years of experience in AI/ML, systems, or platform engineering

- Strong experience in:

- LLMs and generative AI systems





- Building AI-powered tools or platforms

- Designing scalable AI infrastructure (cloud and/or on-prem)

- Experience with:

- Agent frameworks and orchestration systems

- API-based and self-hosted models

- Solid understanding of:

- Data security, privacy, and IP protection in AI systems

- Strong software engineering skills (Python required)

Positive to Have

- Experience working with semiconductor or EDA workflows

- Familiarity with:

- RTL, verification, or physical design flows

- Experience with:

- Air-gapped or secure AI deployments

- GPU clusters and distributed training/inference

- Knowledge of:

- Prompt engineering techniques

- Retrieval-augmented generation (RAG)

- Workflow automation systems

- Exposure to DevOps / MLOps practices

Key Attributes

- Strong systems thinker with end-to-end ownership mindset

- Ability to bridge AI and domain engineering (EDA/SoC)

- Highly proactive with a builder mentality

- Passionate about transforming traditional workflows using AI

- Strong communication and influence across teams

Success in This Role Looks Like

- Adoption of AI across engineering workflows

- Measurable improvements in productivity and quality

- Effective deployment of AI agents across multiple domains

- Secure and scalable AI infrastructure

- Reduced cost and improved efficiency of AI usage

- Engineers enabled to independently build and use AI agents

Why This Role Matters

This is a foundational role in shaping an AI-native semiconductor company. You will define not just tools, but how engineering itself is done, and directly impact the speed, quality, and innovation of our products.

Location

Hybrid / On-site – Bengaluru

📌 AI Platform Architect (Semiconductor Design) (Bengaluru)
🏢 tylsemi
📍 Bengaluru