Software Engineer, AI Engineer (Applied​/Software), Machine Learning​/ ML Engineer

Responsibilities

• Design, develop, and enhance software tools and automation solutions that support engineering workflows in advanced packaging and silicon enablement
• Apply AI/ML techniques end-to-end to improve tool capability, automation, decision support, and workflow efficiency
• Build and integrate intelligent features into software systems, including data-driven automation, predictive capabilities, and workflow optimization
• Collaborate closely with engineering, research, and product teams to understand requirements and translate them into scalable technical solutions
• Contribute to the architecture, design, implementation, testing, and deployment of AI/ML-enabled software tools
• Develop robust, maintainable, and production-quality code in a fast-paced development environment
• Evaluate new AI/ML technologies, frameworks, and approaches to drive innovation in engineering productivity and automation
• Support experimentation, prototyping, and iterative refinement of new solutions in a research-focused setting
• Ensure software solutions are reliable, scalable, and aligned with engineering and business goals
• Participate in code reviews, design discussions, and cross-functional technical problem-solving

• Master’s degree in AI/ML, Data Science, Computer science, Software Engineering, Electrical Engineering
• 6 months of experience in software development skills with experience in building engineering applications, automation tools, or enterprise software solutions
• Hands‑on experience with AI/ML technologies and their practical application in software systems
• Programming languages such as Python, C++, Java, or similar
• Software design, development, testing, and debugging in a collaborative environment
• Hands‑on experience designing, deploying, and maintaining scalable, reliable AI/ML production systems
• End‑to‑end experience with generative AI and LLM‑based solutions, including prompt engineering, embeddings, vector databases, semantic search, and RAG workflows
• Foundations in data structures, algorithms, and software engineering principles
• Hands‑on experience building scalable applications, backend services, APIs, or distributed systems, including integration of ML models into software
• Experience with machine learning systems, including model training, evaluation, inference, and use of frameworks such as PyTorch, Tensor Flow, or scikit‑learn
• Experience deploying AI/ML services using Docker, Kubernetes, cloud platforms, REST/gRPC APIs, or serverless architectures
• Familiarity with MLOps/LLMOps practices, including model versioning, experiment tracking, monitoring, and performance evaluation
• Understanding of system design, distributed computing, performance optimization, security, privacy, and responsible AI principles
• Experience working with large‑scale datasets, data pipelines, or big data/streaming technologies such as Spark, Databricks, or Kafka
• Familiarity with databases (SQL/No
  
  SQL), version control, testing, CI/CD, and cloud‑native development practices