8–10 years of experience in reliability engineering

Proven expertise in statistical modeling, reliability strategy, and aerospace/UAV systems

Experience with survivability engineering under harsh environments and extended missions

Familiarity with aerospace quality standards (AS9100D, DO-160, MIL-STD)

Risk Management: Ability to develop risk registers and mitigation strategies

Lifecycle Analysis: Knowledge of fatigue, durability, and life prediction for UAV systems

Innovation: Exposure to predictive maintenance, AI-driven reliability analytics, and digital twin approaches

Leadership: Mentor junior engineers and build organizational reliability culture

• Reliability Tools: ReliaSoft Weibull++ (probabilistic modelling)
• Statistics: Minitab (statistical analysis and trend prediction)
• PLM: PTC Windchill (data and lifecycle management)
• Analysis: MS Excel (fleet-wide data tracking and KPI reporting)

Bachelor’s / Master’s in Mechanical / Aerospace Engineering

• Reliability Strategy & Architecture
  • Define mechanical reliability strategy for UAV systems
  • Develop fleet-wide statistical reliability models
  • Establish reliability targets and KPIs
• Advanced Reliability Modeling
  • Lead probabilistic and statistical modeling using ReliaSoft Weibull++ and Minitab
  • Predict system-level reliability and failure trends
  • Drive data-driven reliability decisions
• Survivability Engineering
  • Define survivability strategies for harsh environments and extended mission profiles
  • Ensure robustness across operating conditions
• Compliance & Standards Leadership
  • Own compliance with aerospace reliability standards (MIL-STD-417, JSS-55555)
  • Define reliability validation frameworks and audit-ready documentation
• Cross-Functional Leadership
  • Lead collaboration across design, manufacturing, and testing teams
  • Drive reliability-centric design decisions and reviews
• Manufacturing & Field Feedback Loop
  • Integrate manufacturing and field failure data into reliability models
  • Continuously improve product reliability through closed-loop feedback