AI Architect

Mirafra Technologies; Bengaluru; On-site; INR 3,500,000 - 7,500,000; Full time

Job summary

Mirafra Technologies in Bengaluru is seeking an experienced AI Architect to lead the design and integration of AI/ML capabilities across semiconductor engineering workflows. You will bridge deep AI systems expertise with RTL-to-GDSII lifecycle understanding.

You will architect AI-augmented EDA workflows, build internal tooling, and shape AI-differentiated service lines for clients, including agentic AI workflows and multi-agent orchestration across design tasks.

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.
• Experience with deep learning frameworks (PyTorch, TensorFlow) and LLM application development.
• Hands-on experience building production agentic systems with LangGraph, CrewAI, AutoGen/AG2, or Claude/OpenAI SDKs.

Responsibilities

• Define the AI architecture strategy for data pipelines, model selection, and deployment for semiconductor design use cases.
• Architect and prototype AI/ML solutions for backend design, STA, DFT, and physical verification flows.
• Integrate LLMs and ML models into EDA toolchains (Innovus, Tempus, PrimeTime, Calibre) and scripting environments.
• Design multi-agent systems across RTL-to-GDSII with tool calls to EDA engines and internal knowledge bases.
• Build agent infrastructure: interfaces, memory management, state handling, and guardrails for reliability.

Skills

AI architecture

Python

Deep learning

Production ML

Education

Bachelor's/Master's in CS/EE/ECE

Advanced degree preferred

Tools

LangGraph

CrewAI

AutoGen/AG2

Claude Agent SDK

OpenAI Agents SDK

Job description

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.
• Strong 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.
• 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.