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Saviynt

Posted 3 months ago

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Lead AI Engineer

BengaluruRemoteFull-time

AI Summary

Leads design, development, and deployment of AI-powered automation for customer support, building production-grade NLP/LLM solutions and overseeing end-to-end project lifecycles.

About this role

Saviynt's AI-powered identity platform manages and governs human and non-human access to all of an organization's applications, data, and business processes. Customers trust Saviynt to safeguard their digital assets, drive operational efficiency, and reduce compliance costs. Built for the AI age, Saviynt is today helping organizations safely accelerate their deployment and usage of AI. Saviynt is recognized as the leader in identity security, with solutions that protect and empower the world’s leading brands, Fortune 500 companies and government institutions. For more information, please visit www.saviynt.com.

Saviynt - AI-powered identity platform manages and governs human and non-human access to all of an organization's applications, data, and business processes. Customers trust Saviynt to safeguard their digital assets, drive operational efficiency,
and reduce compliance costs. Built for the AI age, Saviynt is helping organizations safely accelerate their AI deployments and use. Saviynt is recognized as the leader in identity security, with solutions that protect and empower the world’s leading brands,
Fortune 500 companies, and government institutions. For more information, please visit
The Staff AI Engineer – Customer Support Automation will drive the design, development, and deployment of AI-powered automation solutions that transform customer support operation. This role combines deep technical expertise in Artificial Intelligence and Machine Learning with strong program and stakeholder management capabilities. The successful candidate will lead cross-functional initiatives, manage end-to-end AI project lifecycles, and deliver measurable improvements in customer experience, operational efficiency, and global scalability.

WHAT YOU WILL BE DOING

  • Design and deploy production-grade AI/ML systems that automate and augment customer support workflows.
  • Build and scale LLM-powered solutions including RAG architectures, intelligent ticket routing, sentiment analysis, predictive insights, and automated response generation.
  • Develop conversational AI systems and agentic workflows for autonomous case handling.
  • Identify high-impact AI leverage points across support operations and translate them into measurable automation initiatives.
  • Continuously evaluate emerging foundation models and AI tooling in the market; benchmark performance, cost, safety, and latency to assess where we stand and recommend adoption when strategically justified
  • Own the full lifecycle of AI initiatives from ideation through deployment, monitoring, and optimization.
  • Establish and mature MLOps practices including model versioning, observability, retraining pipelines, and cost-performance governance.
  • Architect scalable APIs and microservices for AI integration across CRM, ticketing, and knowledge platforms.
  • Partner with Product, Support Operations, Data Engineering, Security, and Leadership teams to deliver measurable business outcomes.
  • Communicate technical architecture decisions and ROI clearly to executive stakeholders

    • WHAT YOU BRING

  • 7+ years of software engineering experience, including 4+ years in AI/ML.
  • Strong hands-on experience with Python and modern ML frameworks (PyTorch, TensorFlow, Hugging Face or equivalent)
  • Proven experience building and deploying NLP/LLM-based systems in production.
  • Hands-on experience designing and implementing RAG architectures and working with vector databases.
  • Experience deploying ML systems in cloud environments (AWS experience, including Bedrock, is a strong advantage but not mandatory)
  • Demonstrated ability to identify where AI can augment, automate, or eliminate operational workflows.
  • Experience building scalable APIs, microservices, and containerized applications (Docker, Kubernetes)
  • Strong understanding of MLOps, monitoring, and production observability.
  • Experience operating in enterprise environments with compliance considerations (SOC2, GDPR, etc.)
  • AI/ML certifications (AWS ML Specialty or equivalent) preferred
  • Strong executive communication skills and ownership mindset

    • Skills

    API DesignAutomationAWS (Bedrock A Plus)Conversational AIDockerGDPR ComplianceHugging FaceKubernetesLLMMicroservicesMLflow Or Similar MLOps ToolingModel VersioningNLPObservabilityPythonPyTorchRAG ArchitecturesRetrain PipelinesSOC2TensorFlowVector Databases

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