AI Platform Architect (Semiconductor Design)

TylSemi; Bengaluru; On-site; INR 11,342,155 - 15,122,873; Full time

Job summary

TylSemi is seeking an AI Platform Architect to lead the integration of AI in semiconductor design. This critical role requires defining and driving AI strategies, architecting AI infrastructure, and educating teams in AI tools. The ideal candidate will have over 8 years of experience in AI/ML and strong knowledge in data security within AI systems. Join us to pioneer transformations in semiconductor engineering with AI at the forefront. Experience in cloud and secure AI systems is vital.

Qualifications

• 8+ years of experience in AI/ML, systems, or platform engineering.
• Strong experience with LLMs and generative AI systems.
• Solid understanding of data security, privacy, and IP protection in AI systems.

Responsibilities

• Define the AI roadmap for semiconductor design workflows.
• Architect and deploy AI infrastructure, including cloud-based and on-prem environments.
• Train engineering teams to effectively use AI tools and agents.

Skills

AI/ML

LLMs

Python

Data security

Systems thinking

Education

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

Tools

Cloud environments (AWS)

EDA tools

Job description

AI Platform Architect (Semiconductor Design)

Role Overview

We are building an AI‑first semiconductor company where AI is deeply embedded in every aspect of engineering—from architecture and RTL design to verification, physical design, and operations. We are looking for a highly capable AI Architect to define and drive our AI strategy, infrastructure, and agent‑based workflows for semiconductor design.

Key Responsibilities

• Define and execute the AI roadmap for semiconductor design workflows across verification, documentation, and output analysis, and identify high‑impact opportunities to improve productivity, quality, and time‑to‑silicon.
• Serve as the central thought leader for AI adoption across the company.
• Architect and deploy AI infrastructure, including cloud‑based (e.g., AWS) and on‑prem (air‑gapped) environments, GPU/compute resource planning, and scaling.
• Build systems for data management, protection, governance, IP security, compliance, and auditability of AI‑generated outputs.
• Design and implement AI agents and multi‑step pipelines that interact with EDA tools and integrate across RTL, DV, and physical design flows, and build reusable agent frameworks and orchestration layers.
• Define and enforce AI guardrails, including data privacy, IP protection, token usage and cost optimization, and access policies, ensuring alignment with enterprise‑grade security standards.
• Evaluate and recommend LLMs and AI tools for documentation, data analysis, and continuous benchmarking of model selection across performance, cost, and privacy constraints.
  Stay current with advancements in LLMs, agent frameworks, and AI tooling ecosystem.
• Train engineering teams to effectively use AI tools and agents, build custom AI agents, create playbooks and templates for AI‑assisted workflows, and drive a culture of AI‑native engineering.

Required Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field.
• 8+ years of experience in AI/ML, systems, or platform engineering.
• Strong experience with LLMs and generative AI systems, building AI‑powered tools or platforms, and designing scalable AI infrastructure (cloud and/or on‑prem).
• Experience with agent frameworks, orchestration systems, API‑based and self‑hosted models.
• Solid understanding of data security, privacy, and IP protection in AI systems.
• Strong software engineering skills, including Python.

Preferred Qualifications

• 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, retrieval‑augmented generation (RAG), and workflow automation systems.

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 Metrics

• Adoption of AI across engineering workflows.
• Measurable improvements in productivity and quality.
• Secure and scalable AI infrastructure deployment.
• 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.