Role Overview

We are seeking an

AI Architect to lead the design and integration of AI/ML capabilities across our semiconductor engineering workflows and service offerings. The role involves bridging deep AI/ML systems expertise with hands-on understanding of the chip design lifecycle (RTL-to-GDSII, DFT, verification, sign-off). The ideal candidate will architect AI-augmented EDA workflows, build internal AI tooling, and shape AI-differentiated service lines for our clients.

As agentic AI reshapes how engineering work gets done, this role will pioneer how autonomous and semi-autonomous agents are embedded into our design and verification flows — moving beyond AI-as-assistant toward AI-as-collaborator.

Key Responsibilities

Define the AI architecture strategy spanning data pipelines, model selection, and deployment for semiconductor design use cases. Architect and prototype AI/ML solutions that augment backend design, STA, DFT, and physical verification flows (e.g., PPA optimization, automated DFT insertion, log/report analysis, predictive timing closure). Integrate LLMs and ML models into existing EDA toolchains (Innovus, Tempus, PrimeTime, Calibre, etc.) and Linux/TCL/Python scripting environments.

Architect agentic AI workflows

— agents that plan, execute, and iterate across multi-step design tasks (debug triage, ECO loop automation, constraint generation, regression analysis) with appropriate human-in-the-loop checkpoints. Design multi-agent systems that orchestrate specialized agents across the RTL-to-GDSII flow, integrating tool calls to EDA engines, scripts, and internal knowledge bases.

Build robust agent infrastructure: tool/function-calling interfaces, context and memory management, state handling, and guardrails for reliability in engineering-critical applications. Evaluate and benchmark foundation models, fine-tuning approaches, RAG pipelines, and agent orchestration patterns. Establish an agent control plane

— policy, approval gates, observability,



and audit trails — before autonomous agents touch production systems. Partner with delivery teams and clients to translate AI capabilities into productized service offerings. Establish MLOps practices, model governance, and scalable infrastructure (on-prem/cloud/hybrid). Mentor engineers on AI/ML adoption and build the technical foundation for an AI Center of Excellence.

Required Qualifications Bachelor's/Master's in CS, ECE, EE, or related field; advanced degree preferred. 10+ years in software/AI engineering, with

3+ years architecting production AI/ML systems . Demonstrated experience with deep learning frameworks (PyTorch, TensorFlow), LLM application development, and modern ML infrastructure. Hands-on experience building production agentic systems with one or more current frameworks — e.g.,

LangGraph

(graph-based, strong for stateful production workflows),

CrewAI

(role-based multi-agent crews),

AutoGen/AG2

(conversational multi-agent), or the

Claude Agent SDK / OpenAI Agents SDK for model-native agents. Working knowledge of the

Model Context Protocol (MCP)

for connecting agents to tools and data sources, and familiarity with agent-to-agent coordination patterns. Strong command of tool-use/function-calling patterns , ReAct-style planning loops, RAG, and agent evaluation/observability. Solid proficiency in Python; familiarity with data engineering and MLOps tooling. Working knowledge of the semiconductor/chip design flow

— RTL, synthesis, P&R;, STA, DFT, and/or physical verification.

Preferred Qualifications Hands-on background in VLSI/backend design or EDA tool development.

Experience with EDA-vendor AI offerings, AI-assisted design automation, or agentic coding tools. Exposure to advanced nodes (2nm, 3nm, 5nm.....22nm) and ARM-based SoC programs. Familiarity with agent reliability engineering — failure recovery, retries, runaway-tool-call prevention, and audit readiness. Track record of building AI products or service lines in an engineering services context.

📌 AI Architect (Bengaluru)
🏢 Mirafra Technologies
📍 Bengaluru