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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
₹0 - ₹0 a year
Key Responsibilities
1. AI Development & Architecture Leadership
. Design and implement AI/ML models for:
.Intelligent ticket routing
.Sentiment analysis
.Predictive support insights
.Automated response generation
.Develop conversational AI solutions (chatbots and virtual assistants) using foundation models such as Claude, Titan, and Llama.
.Build and manage Retrieval-Augmented Generation (RAG) knowledge systems with semantic search and intelligent recommendations.
.Architect and implement agentic AI workflows for autonomous support automation.
.Establish and mature MLOps practices including:
.Model versioning
.Monitoring and observability
.Retraining pipelines
.Performance optimization
. Lead technical architecture decisions across AI infrastructure and cloud services
.Continuously research and evaluate emerging AI advancements to proactively identify and implement optimal, cost-effective solutions that scale our current AI Agent ecosystem and significantly boost productivity.
2. Agile Delivery & Project Ownership
. Own end-to-end delivery of AI automation initiatives—from ideation through production deployment.
.Define scope, timelines, milestones, and resource plans with clear accountability.
.Lead agile ceremonies:
.Sprint planning
.Daily stand-ups
.Retrospectives
.Executive demos
.Proactively manage risks, dependencies, and technical blockers.
.Coordinate cross-functional teams including data engineers, support operations, product managers, and leadership stakeholders.
.Communicate technical decisions and business impact clearly to executive leadership.
3. Engineering Excellence & Platform Integration
.Integrate AI solutions with existing support ecosystems (CRM, ticketing systems, knowledge bases)
.Build scalable APIs and microservices for AI deployment and consumption.
.Develop robust data pipelines for:
.Training data ingestion
.Preprocessing
.Feature engineering
.Enforce engineering best practices:
.Code quality standards
.Documentation
.Peer reviews
.Testing frameworks
.Implement production-grade monitoring and observability for AI systems.
.Partner with security and compliance teams to ensure responsible AI governance.
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