AI Engineer Global IT Infrastructure

The AI Engineer supports end-to-end AI/ML delivery in Vishay’s Office of Innovation, working with platform teams on model development, generative AI applications, infrastructure automation and governance tooling. The role is based in Pune, India.

Responsibilities

• Build, train and evaluate ML models from data preparation through deployment. Perform exploratory analysis, feature engineering and experiments; document findings and support validation, testing and performance benchmarking.
• Develop GenAI applications such as chatbots, summarization tools, document-processing pipelines and internal copilots. Implement prompt patterns, retrieval-augmented generation pipelines and tool-augmented agents; evaluate new LLM capabilities and contribute to proofs of concept.
• Support ML pipelines for ingestion, preprocessing, automated training and model serving. Monitor deployed models for degradation, drift and anomalies, and contribute infrastructure-as-code for cloud AI workloads.
• Produce model cards, data-lineage records and risk-assessment inputs. Implement monitoring and logging for auditability and compliance, and flag potential bias, fairness or privacy concerns.
• Collaborate with data engineers, platform engineers and business analysts to integrate AI outputs into existing systems. Participate in Agile planning and design discussions, and write testable code and technical documentation.

Required qualifications

• 3–5 years of software engineering experience, including at least 1 or 2 years in ML- or AI-focused roles. A bachelor’s degree in computer science, engineering, mathematics or a related field, or equivalent demonstrable experience.
• Hands-on experience with scikit-learn, PyTorch or TensorFlow, pandas and NumPy; understanding of supervised and unsupervised learning, model evaluation, overfitting and feature engineering.
• Working knowledge of OpenAI, Anthropic or Hugging Face LLM APIs and at least one orchestration framework, such as LangChain or LlamaIndex. Familiarity with AWS, Azure or GCP, Docker, Git, basic CI/CD and collaborative development.

Preferred qualifications

• Exposure to MLflow, DVC or Weights & Biases; vector databases such as Pinecone, Weaviate, ChromaDB or pgvector for semantic search or RAG; responsible AI, bias detection or model explainability tools; associate-level or higher cloud certification; or enterprise IT experience involving ITSM, infrastructure or networking.