Delivery Manager – Multimodality LLM Engagement

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Requirements:

• 4–8 years of experience in a Delivery Manager, Program Manager, or similar role within a technical or data-driven environment.
• Proven track record in managing end-to-end project lifecycles, scaling teams, and optimizing delivery pipelines.
• Strong experience in client-facing roles involving requirements gathering, delivery tracking, and stakeholder alignment.
• Experience managing diverse and distributed teams.
• Skilled in driving team performance, managing escalation workflows, and balancing speed, quality and cost.
• Working knowledge of multimedia modalities and the ability to interact with engineering and product managers to monitor and maintain output quality.
• Proficient in using project management and tracking tools (e.g., Airtable, Notion, JIRA, Asana, Google Sheets).

Responsibilities:

• Own full project lifecycle from kickoff and scoping to delivery and stabilization.
• Manage multiple concurrent LLM training streams (Evals, SFT, RLHF, RLEF, etc.,) across languages and domains.
• Lead and coordinate distributed remote teams including AI trainers, team leads and engineering managers.
• Maintain strong alignment with Engineering Managers, ensuring delivery of technically sound and review-compliant datasets.
• Monitor throughput, quality, rework, review coverage and staffing requirements.
• Act as the strategic point of contact for clients - gathering requirements, aligning on expectations, and managing feedback loops.
• Build and maintain detailed project trackers, dashboards, and delivery health reports.
• Proactively flag risks and drive resolution to ensure uninterrupted, high-quality delivery.
• Set up scalable processes, SOPs, and review systems to mature project operations.
• Own delivery-level quality KPIs across all roles from trainers to engineers.
• Ensure clarity, accuracy, and completeness in outputs: code, responses, explanations, and evaluations.
• Work closely with team leads to implement quality review loops and resolve systemic quality gaps.
• Identify inefficiencies and continuously optimize workflows and operational structure.
• Ensure all output meets the highest standards expected by AI researchers and clients.