AI/ML Engineer

About the Role Seeking a hands-on technical AI/ML engineer to join our growing team and solve exciting engineering problems in the semiconductor space. This position will work closely with a multidisciplinary team, product managers, data engineers, data scientists, and business stakeholders to bring AI solutions to production. What You'll Do

• Design and develop ML models for high-impact engineering solutions across NLP, computer vision, and predictive analytics.
• Build, train, and optimize ML/DL models using Python, PyTorch, LangChain, and RAG, with capability to run on cloud or on premises.
• Manage data collection and preprocessing for structured and unstructured data (numeric, images, videos, documents).
• Perform feature engineering to extract and transform relevant features to improve model performance and interpretability.
• Evaluate and monitor models using statistical metrics and validation techniques.
• Deploy and integrate models using Kubernetes, Flask, Ray Serve, Azure DevOps, ONNX, or cloud-based solutions.
• Stay abreast of AI/ML research; experiment with new algorithms, tools, and frameworks to drive innovation.
• Document model architecture, data sources, training processes, and evaluation metrics; present findings to technical and non-technical audiences.
• Uphold ethical standards and ensure compliance with data privacy, security, and responsible AI deployment. What We're Looking For
• Education: Bachelor's or Master's in Computer Science, Engineering, Mathematics, Statistics, or related field. 3+ years of professional experience in ML/AI; PhD or relevant research experience would be a plus.
• Hands-on experience with neural networks, deep learning, architectures such as CNNs, RNNs, Transformers, and Generative AI.
• Exposure to MLOps practices: monitoring, scaling, and automating ML workflows.
• Experience with big data platforms: Databricks, Hadoop, Spark, Dataflow, etc.
• Proficiency in programming languages: Python (preferred), Java, C#, or C++.
• Deep understanding of ML frameworks: PyTorch, scikit-learn, Keras, etc.; data manipulation tools: NumPy, SQL, Pandas.
• Solid grasp of statistics, probability theory, and linear algebra.
• Familiarity with reinforcement learning, generative models, or explainable AI. Nice to Have
• PhD or relevant research experience would be a plus.