Senior R&D AI Software Development Engineer

Keysight Technologies; Singapore; On-site; SGD 120,000 - 240,000; Full time

Job summary

Anite in Singapore seeks a Senior AI Software Development Engineer to build agentic AI capabilities linking LLMs, EDA tools, simulation engines and APIs. You will enable goal-driven automation, tool invocation, and reasoning over results to assist repeatable design-analysis tasks.

The role emphasizes production-grade backend services in Python, MCP architecture, and collaboration across multi-disciplinary teams to accelerate engineering workflows with AI-driven automation.

Qualifications

• Master’s degree or PhD in Computer Science, Software Engineering, or related field.
• Strong Python software-engineering skills for production-grade services.

Responsibilities

• Develop agentic AI capabilities connecting LLMs, EDA tools, APIs, knowledge sources and user workflows.
• Enable supervised, goal-driven automation where AI agents plan, invoke tools, reason over results, and assist with design-analysis tasks.
• Build MCP-enabled agent infrastructure: servers, clients, skills, tool schemas, orchestration, guardrails, evaluation frameworks.
• Translate EDA workflow use cases into reliable AI-agent capabilities: simulation setup, diagnostics, optimization, report generation, guided automation.

Skills

Python

LLMs

AI-agent frameworks

CI/CD

Agile

Git

Education

Master’s degree or PhD in CS/SE

Tools

MCP

Vector databases

Knowledge graphs

Prompts design

Job description

You will join a high-performing Keysight R&D team developing next-generation Electronic Design Automation (EDA) workflow solutions for High-Speed Digital (HSD), signal integrity, power integrity, and adjacent simulation-driven engineering domains. The team combines product APIs, simulation knowledge, AI/ML methods, neural-network modeling, and agentic workflow automation to help engineers interact more efficiently with complex design tools.

About the Job

As a Senior AI Software Development Engineer, you will develop agentic AI capabilities that connect large language models, EDA applications, simulation engines, product APIs, knowledge sources, and user workflows. Your work will enable supervised, goal-driven automation where AI agents can plan, invoke tools, reason over results, and assist with repeatable design-analysis tasks.

• Build and maintain MCP-enabled agent infrastructure, including servers, clients, skills, tool schemas, orchestration services, guardrails, and evaluation frameworks.
• Translate EDA workflow use cases into reliable AI-agent capabilities, such as simulation setup assistance, design diagnostics, optimization workflows, compliance-test setup, report generation, and guided automation.
• Apply neural-network techniques where appropriate, including surrogate modeling, embeddings, vector search, model evaluation, physics-aware or data-driven models, and AI-based acceleration of expensive simulation workflows.
• Develop production-quality backend services in Python and/or other modern languages with strong attention to reliability, observability, testing, maintainability, and secure deployment.

Technical Qualifications

• Master’s degree or PhD in Computer Science, Software Engineering, or a related field.
• Strong software engineering skills in Python, with experience building production-grade services, libraries, automation tools, or developer-facing APIs.
• Hands-on experience with AI-agent or copilot-style solutions using LLMs, tool calling/function calling, planner-executor patterns, retrieval-augmented generation, and agent orchestration frameworks.
• Practical knowledge of Model Context Protocol (MCP), including server/client concepts, tools, resources, prompts, schema design, secure tool invocation, and local or remote deployment patterns.
• Experience with AI/ML and neural-network concepts relevant to engineering workflows, such as deep learning, surrogate models, embeddings, vector databases, structured outputs, prompt engineering, and context management.
• Strong grasp of modern software-development practices: Agile delivery, Git-based workflows, code review, CI/CD, automated testing, package management, security reviews, and documentation.

Preferred Qualifications

• Experience with EDA, CAD, simulation, high-speed digital design, signal integrity, power integrity, RF/microwave design, photonics, or scientific/engineering software.
• Experience with product APIs, desktop application automation, Python scripting inside engineering tools, or integration of AI agents with domain-specific software environments.
• Familiarity with neural-network-based surrogate modeling for accelerating simulation, design-space exploration, optimization, or rapid what-if analysis.
• Familiarity with LLM application frameworks, agent frameworks, vector databases, knowledge graphs, semantic search, workflow orchestration platforms, or cloud-based deployment environments.
• Strong collaboration skills across multi-site and multi-cultural teams, including R&D, product management, application engineering, and customer-facing stakeholders.
• Clear written and spoken English communication, with the ability to explain AI-agent architecture, neural-network trade-offs, and engineering workflow impact to both technical and management audiences.
• Ability to guide development teams, mentor junior engineers, and drive pragmatic adoption of AI in production engineering software.