Abhishek K. Bais

Led the product design and development of PowerArtist's AI/ML-guided RTL power estimation engine, improving runtime by 500X, and facilitating early decision-making for SoC design customers.

· Drove product adoption across Tier 1/2 customers by conducting targeted user research, gathering continuous customer feedback, resolving critical deployment issues, and leading cross-functional collaboration with R&D, product, and sales teams, while delivering hands-on training to 50+ SoC design engineers, contributing to $1M+ YoY revenue growth and improved product stickiness.

Built KPI-driven validation harnesses to automate test coverage and tracking, ensuring tool robustness and reliability, boosting QA throughput by 30–40% across SoC workloads.

· Led product design and development of PrimeTime's physically aware ECO solution, reducing timing and power signoff runtimes from days to hours, shaving 10-20 days off time to market designs.

· Drove product innovation and scaled customer adoption by translating Tier 1/2 customer feedback and competitive insights into a clear product vision and prioritized roadmap epics, ensuring the on-time delivery of differentiated capabilities. These enhancements reinforced PrimeTime’s leadership in the advanced sign-off market and elevated its value proposition across complex SoC design flows.

Accelerated cross-functional execution velocity by mentoring junior R&D engineers, leading agile onboarding sprints, and scaling fluency in the domain area of physical ECO and optimization, resulting in increased engineering throughput across the product development lifecycle, and faster rollout of roadmap-aligned features.

· Co-led the product development of Talus’s GPU-parallelized, MCMM-aware algorithms for sub-90nm physical optimization and timing analysis, incorporating data analytics to guide performance tuning and architectural decisions, reducing runtime from 200 to 30–50 hours on 500M-gate SoCs, achieving 3X-5X speedup, enabling scalable adoption in high-complexity customer design flows.

Translated customer feedback into prioritized roadmap features, and drove delivery through prototype validation, alpha/beta testing, and quality benchmarks, resulting in 20–40% faster onboarding, 10–20% higher retention, and 30–50% fewer support cases.

Led product development of Veloce's distributed task graph scheduling system as the unified interface with an intuitive UI for HAV workflows, enabling overlapped test execution across low-cost farm machines, boosting throughput by 16X, cutting debug cycles, and reducing verification costs.

Defined and scaled core HAV methodologies through pattern-based simulation and switching activity feedback flows, using performance and debug metrics to achieve a 2X–5X simulation speedup, a 30–50% reduction in debug triage time, and a 40% increase in emulator utilization, driving broader adoption and deeper integration across Tier 1/2 customer deployments.

Developed HDL Designer Series’s relaxed grammar–based System Verilog parsing and dialect detection in C/C++, reflecting the product vision of accelerating early RTL debug through graphical visualization of incomplete or inconsistent code, resulting in a 30–50% reduction in rework time, saving 5–10 engineering days per week, and improving first-pass design readiness.

• Co-led product development of 'Mantra 1.0,' a machine-assisted NLP-based English-to-Hindi translation system using grammar-based parsing and language modeling, a later version of which is deployed as the official real-time translation platform in the Indian Parliament, enhancing national linguistic accessibility, and showcasing AI's institutional adoption in mission-critical environments, with millions of live session translations processed to date.