AI-Technical Project Manager

About the Role

research and Development knowledge and worked on Fast phase startup and knowledge on how Speech and LLM model works

Responsibilities

Project Manager – Speech AI & LLM Engineering

Speech AI Delivery | LLM Project Management | AI Engineering Execution | Cross-Functional Delivery

| Location | Bangalore (Work from Office – 5 days) | Experience | 5 to 8 years | Type | Full-Time | Reports To | VP of AI Engineering |

What You Will Do

• AI Project Delivery: Drive delivery across ASR, TTS, speech intelligence, LLM applications, RAG pipelines, agentic workflows, conversational AI, and related AI capabilities.
• Agile Execution: Run Scrum/Kanban ceremonies including sprint planning, stand-ups, reviews, retrospectives, and backlog grooming across AI engineering squads.
• Roadmap & Milestone Tracking: Maintain the Speech AI and LLM roadmap, track milestones, and ensure clear visibility on timelines, risks, blockers, and dependencies.
• Backlog Management: Work with Speech R&D, LLM, Product, and Engineering leads to prioritize backlog items based on customer impact, model performance gaps, technical dependencies, and business priorities.
• Release Planning: Coordinate model, API, and product releases across teams, including release readiness, QA status, evaluation results, deployment dependencies, and customer rollout plans.
• Model Evaluation Coordination: Track evaluation activities for ASR, TTS, and LLM systems, including WER/CER, latency, MOS, accuracy, hallucination rate, response quality, task completion, safety, regression testing, and real-world performance feedback.
• Customer Feedback Loop: Build a structured feedback layer between model users, customer-facing teams, product teams, Speech R&D, and LLM teams so that production issues are converted into actionable engineering tasks.
• Cross-Team Collaboration: Act as the connective layer between Speech R&D, LLM teams, Platform Engineering, Product, QA, Data Engineering, MLOps, Delivery, and Customer Success teams.
• Risk & Dependency Management: Identify risks early across data, model development, prompt engineering, evaluation, infra, deployment, integration, and customer timelines; drive closure with clear owners and action items.
• Documentation: Own project documentation, meeting notes, decision logs, release notes, model improvement trackers, evaluation reports, and process artifacts.
• Metrics & Reporting: Track sprint velocity, cycle time, release progress, model performance metrics, customer issues, and delivery KPIs; provide data-driven updates to leadership.

Requirements

Must Have	Good to Have

• 5–10 years of experience managing software/AI engineering delivery, with strong exposure to Agile methodologies (Scrum, Kanban, SAFe)
• Proven track record of sprint planning, execution, and estimation across multiple engineering squads
• Hands-on backlog prioritization and release planning in fast-moving, cross-functional environments
• Experience running retrospectives and driving measurable continuous improvement
• Strong planning & organization skills: prioritization, roadmap planning, milestone tracking, and dependency management
• Solid documentation, decision-log, and process-artifact hygiene
• Strong analytical, problem-solving, and root-cause thinking; comfort with delivery KPIs (velocity, cycle time, throughput)
• Excellent stakeholder communication across engineering, product, AI research, and leadership | - Certification in Scrum (CSM/PSM), SAFe (SA/SPC), or PMP/PMI-ACP
• Experience delivering ASR, TTS, LLM, RAG, or agentic/conversational AI products
• Familiarity with model evaluation metrics (WER/CER, MOS, latency, hallucination rate, task completion)
• Experience with tools like Jira, Confluence, Linear, or Azure DevOps
• Exposure to BFSI, telecom, healthcare, or enterprise product domains
• Familiarity with MLOps, data pipelines, APIs, cloud deployments, and CI/CD to converse fluently with AI and platform teams
• Experience setting up or scaling PMO practices in a startup environment |