Ratnesh Patidar

Operations and AI Strategy professional with 3+ years of experience managing enterprise client portfolios, leading cross-functional teams, and delivering AI annotation solutions. Proven track record in client retention, operational process improvement, and team development. Pursuing an MBA to deepen expertise in operations and project management, aiming to lead tech-driven operations at scale.

Lead Manager, Client Strategy & Business Operations - Standard AI - San Francisco, CA (Remote)

(2023-09)

• Retained 85% of an enterprise client portfolio of 20+ clients by implementing structured quarterly business reviews, proactive risk escalation protocols, and tailored solution alignment sessions — exceeding the industry benchmark of ~70% retention for AI service firms.
• Drove new business growth by identifying upsell opportunities within existing accounts, building custom solution decks, and leading end-to-end deal negotiations, contributing to recurring revenue expansion across the portfolio.
• Reduced cross-team project delivery delays by standardising a weekly sync cadence across engineering, annotation, and client-facing teams using RACI frameworks, improving on-time delivery rates for active client projects.
• Coordinated go-to-market campaigns for AI product offerings by aligning product capabilities with sector-specific client pain points, increasing inbound interest from target verticals.

Computer Vision Annotation Expert & Subject Matter Expert - Datapure Technology LLP - Indore, MP

(2022-01 - 2023-08)

• Achieved 92% annotation accuracy across multiple AI training datasets by establishing quality benchmarks, running structured audit cycles, and delivering SME-level feedback — directly improving downstream AI model performance for client deployments.
• Cut annotation turnaround time by ~30% by redesigning the labelling pipeline, standardising QA checkpoints, and introducing pre-labelling tools, enabling the team to scale throughput without additional headcount.
• Mentored and managed a team of 18 junior annotators by designing onboarding playbooks and running bi-weekly calibration sessions, reducing individual error rates and cutting average ramp-up time from 6 weeks to 3 weeks.
• Collaborated with product and engineering teams to define annotation requirements, document user workflows, and implement system updates, ensuring annotation outputs met evolving model training specifications.

Master of Business Administration (MBA) ( Distance Learning) - Operations, Strategy, AI&ML - Dr. D. Y. Patil Vidyapeeth (2023 - 2025)

Bachelor of Technology (B.Tech) - Computer Science & Engineering (CSE) - Sushila Devi Bansal College of Technology (2018 - 2022)