Key Responsibilities
Product Strategy & Vision
·
Define product vision and roadmap for cloud,
hybrid, and on-prem deployments (short-term releases and long-term goals)
·
Identify opportunities
to modernize legacy on-prem systems while enabling cloud migration paths.
·
Lead discovery using customer interviews,
market analysis, and competitive benchmarks.
Execution & Delivery
·
Drive sprint execution using Agile
methodologies, ensuring clear alignment between product and engineering teams.
·
Oversee release management, ensuring adherence
to timelines, quality, and performance standards.
·
Ensure product
readiness across usability, performance, and design benchmarks
·
Collaborate on architecture decisions and act
as a technical authority and design reviewer, setting architectural standards,
best practices, and engineering guidelines.
Cloud & Hybrid Infrastructure Management
·
Shape product capabilities across
Azure/AWS/GCP as well as on-premise environments.
·
Work with DevOps and infra teams to define
SLAs, deployment models (cloud, hybrid, on-prem), and reliability requirements.
·
Oversee cost optimization for cloud and
resource utilization optimization
for on-prem servers.
On‑Prem Server Management
·
Collaborate with infra/ops teams to define:
o
Computer/server specifications
o
Capacity planning
o
Patch/update cycles
o
Backup & disaster recovery policies
o
High-availability and failover strategies
·
Ensure product compatibility with customer
on-prem server environments, including restricted or air-gapped networks.
·
Prioritize features enabling easier
installation, upgrades, and lifecycle management for on-prem customers.
Database Performance & Optimization
·
Work with engineering/DBA teams to optimize the database
performance:
·
Drive initiatives to improve database
scalability and availability across SQL (MySQL) and NoSQL systems.
·
Ensure database architecture supports AI
workloads, telemetry, search, and analytics.
GenAI & Data Intelligence Features
·
Lead integration of LLMs using Azure
OpenAI/AWS Bedrock or custom models.
·
Ensure responsible AI practices, safety
guardrails, data privacy, and governance.
·
Define evaluation frameworks, model
monitoring, fail-safe mechanisms, and auditability.
Go-to-Market & Commercialization
·
Lead launch planning, pricing models,
packaging, and customer enablement.
·
Collaborate with pre-sales, solution
engineers, and customer success teams.
·
Own adoption, churn reduction, and feature usage KPIs.
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