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AI Engineer Global IT Infrastructure

Vishay Intertechnology
Pune, IND
Full-time
Mid · 3–5 years experience
Onsite
Discovered 2 weeks ago
awsazuredockergcpgithuggingface

Full Job Posting

Key Responsibilities

  • AI/ML Model Development • Build, train, and evaluate ML models, contributing to the full model lifecycle from data preparation through to deployment. • Conduct exploratory data analysis, feature engineering, and model experimentation; document findings clearly. • Support model validation, testing, and performance benchmarking activities. Generative AI & LLM Applications • Develop and maintain GenAI-powered applications including chatbots, summarization tools, document processing pipelines, and internal copilots. • Implement prompt engineering patterns, retrieval-augmented generation (RAG) pipelines, and tool-augmented agents using established frameworks. • Participate in the evaluation of new LLM capabilities and contribute to internal proof-of-concepts.
  • AI Infrastructure & MLOps • Support the building and maintenance of ML pipelines, including data ingestion, preprocessing, training automation, and model serving. • Manage and monitor deployed models; identify and escalate performance degradation, drift, or anomalies. • Contribute to infrastructure-as-code for AI workloads across cloud environments (AWS, Azure, or GCP). AI Governance & Quality • Assist in producing governance documentation: model cards, data lineage records, and risk assessment inputs. • Implement monitoring and logging frameworks to support auditability and compliance requirements. • Apply responsible AI checklists and flag potential bias, fairness, or privacy concerns during development.
  • Collaboration & Delivery • Work closely with data engineers, platform engineers, and business analysts to integrate AI outputs into existing systems. • Participate in Agile ceremonies, sprint planning, and technical design discussions. • Write clean, well-documented, testable code and maintain internal technical documentation.
  • Qualifications Essential • 3 to 5 years of software engineering experience with at least 1 or 2 years in ML or AI-focused roles. • Solid skills with hands-on experience in ML libraries (scikit-learn, PyTorch or TensorFlow, pandas, NumPy). • Working knowledge of LLM APIs (OpenAI, Anthropic, HuggingFace) and at least one orchestration framework (LangChain, LlamaIndex). • Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization basics (Docker). • Experience with version control (Git), basic CI/CD practices, and collaborative development workflows. • Understanding of core ML concepts: supervised/unsupervised learning, model evaluation, overfitting, and feature engineering. • Bachelor’s degree in computer science, Engineering, Mathematics, or a related field — or equivalent demonstrable experience.
  • Desirable • Exposure to MLOps tools such as MLflow, DVC, or Weights & Biases. • Experience with vector databases (Pinecone, Weaviate, ChromaDB, pgvector) for semantic search or RAG applications. • Familiarity with responsible AI principles, bias detection methods, or model explainability tools. • Cloud certifications (AWS, Azure, or GCP) at associate level or above. • Experience in an enterprise IT environment (ITSM, infrastructure, networking) is a plus.
  • AI/ML Model Development
  • Build, train, and evaluate ML models, contributing to the full model lifecycle from data preparation through to deployment.
  • Conduct exploratory data analysis, feature engineering, and model experimentation; document findings clearly.
  • Support model validation, testing, and performance benchmarking activities.
  • Generative AI & LLM Applications
  • Develop and maintain GenAI-powered applications including chatbots, summarization tools, document processing pipelines, and internal copilots.
  • Implement prompt engineering patterns, retrieval-augmented generation (RAG) pipelines, and tool-augmented agents using established frameworks.
  • Participate in the evaluation of new LLM capabilities and contribute to internal proof-of-concepts.
  • AI Infrastructure & MLOps
  • Support the building and maintenance of ML pipelines, including data ingestion, preprocessing, training automation, and model serving.
  • Manage and monitor deployed models; identify and escalate performance degradation, drift, or anomalies.
  • Contribute to infrastructure-as-code for AI workloads across cloud environments (AWS, Azure, or GCP).
  • AI Governance & Quality
  • Assist in producing governance documentation: model cards, data lineage records, and risk assessment inputs.
  • Implement monitoring and logging frameworks to support auditability and compliance requirements.
  • Apply responsible AI checklists and flag potential bias, fairness, or privacy concerns during development.
  • Collaboration & Delivery
  • Work closely with data engineers, platform engineers, and business analysts to integrate AI outputs into existing systems.
  • Participate in Agile ceremonies, sprint planning, and technical design discussions.
  • Write clean, well-documented, testable code and maintain internal technical documentation.

Qualifications

  • Essential
  • 3 to 5 years of software engineering experience with at least 1 or 2 years in ML or AI-focused roles.
  • Solid skills with hands-on experience in ML libraries (scikit-learn, PyTorch or TensorFlow, pandas, NumPy).
  • Working knowledge of LLM APIs (OpenAI, Anthropic, HuggingFace) and at least one orchestration framework (LangChain, LlamaIndex).
  • Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization basics (Docker).
  • Experience with version control (Git), basic CI/CD practices, and collaborative development workflows.
  • Understanding of core ML concepts: supervised/unsupervised learning, model evaluation, overfitting, and feature engineering.
  • Bachelor’s degree in computer science, Engineering, Mathematics, or a related field — or equivalent demonstrable experience.
  • Desirable
  • Exposure to MLOps tools such as MLflow, DVC, or Weights & Biases.
  • Experience with vector databases (Pinecone, Weaviate, ChromaDB, pgvector) for semantic search or RAG applications.
  • Familiarity with responsible AI principles, bias detection methods, or model explainability tools.
  • Cloud certifications (AWS, Azure, or GCP) at associate level or above.

Preferred Qualifications

  • Experience in an enterprise IT environment (ITSM, infrastructure, networking) is a plus.

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