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Principal AI Engineer – Core AI Services

AVEVA · Bangalore

New
Senior 🇬🇧 English
Microsoft Azure Azure Functions AKS API Management RESTful API design Large Language Models prompt engineering Model Context Protocol Agent2Agent regression classification clustering deep learning AI ethics GitHub Copilot AI Foundry Semantic Kernel MAF

Job description

About the role

AVEVA is seeking a Principal AI Engineer to join its Core AI Services team in Bangalore. You will design, develop, and scale secure, cloud‑native AI platform services and public APIs that become the foundation for AI adoption across AVEVA’s product portfolio and partner ecosystem.

Key responsibilities

  • Build scalable, fault‑tolerant services on Microsoft Azure, ensuring high performance and reliability.
  • Develop secure, well‑documented public APIs and SDKs for internal and external developers.
  • Collaborate with cross‑functional teams to deliver end‑to‑end solutions across data pipelines, orchestration, and service APIs.
  • Embed robust security controls to protect sensitive data and enforce secure access.
  • Participate in design reviews, code reviews, and architectural discussions.
  • Mentor junior developers and foster a culture of continuous learning.
  • Work on AI model deployment, training, and tooling development.

Required profile

  • 8+ years of professional software engineering experience, with at least 3 years focused on AI/ML systems.
  • Strong hands‑on experience with Microsoft Azure PaaS services (Azure Functions, AKS, API Management).
  • Proven ability to design, version, test, and manage RESTful APIs.
  • Deep understanding of Large Language Models, Retrieval‑Augmented Generation, prompt engineering, and AI orchestration tools.
  • Knowledge of AI ethics, regulations (e.g., NIST AI RMF, EU AI Act) and commitment to responsible AI.

Required skills

  • Microsoft Azure (Azure Functions, AKS, API Management)
  • RESTful API design, versioning, testing, lifecycle management
  • Large Language Models (LLMs) and performance‑cost trade‑offs
  • Retrieval‑Augmented Generation (RAG), prompt engineering, tool calling
  • Model Context Protocol (MCP) and Agent2Agent (A2A) standards
  • Machine‑learning algorithms (regression, classification, clustering, deep learning)
  • AI ethics frameworks (NIST AI RMF, EU AI Act)
  • GitHub Copilot and AI development tools (AI Foundry, Semantic Kernel, MAF)

Questions fréquentes

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Source : ats:workday

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Published 1 day ago

Expires 1 month from now

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AVEVA

Bangalore