Technical Project Manager

About the Role Nuvo AI is looking for a Technical Project Manager to drive the planning, execution, technical coordination, and delivery of complex AI/GenAI and software engineering projects. The role requires a strong technical background and the ability to work closely with AI/ML Engineers, Full-Stack Developers, Data Engineers, and QA/SDET teams. This is not a traditional project coordination role. We are looking for someone who understands software architecture, AI/GenAI, APIs, databases, data pipelines, search, testing, and modern application development, and can translate business/product requirements into executable technical plans. The Technical Project Manager will act as the technical bridge between Product/Business stakeholders and engineering teams, ensuring that technical dependencies are identified, risks are managed, teams are aligned, and projects are delivered successfully. Required Skills & Experience Must Have

• 7–12 years of overall experience in software/technology projects.
• Strong technical background in software engineering, architecture, AI/ML, or data engineering.
• 3+ years of experience in Technical Project Management / Technical Program Management / Technical Delivery / Engineering Leadership.
• Experience managing cross-functional technical teams.
• Experience working with AI/ML or Generative AI projects.
• Strong understanding of LLMs, RAG, embeddings, vector databases, or AI-powered applications.
• Good understanding of Full-Stack application architecture.
• Good understanding of data pipelines, databases, APIs, and search technologies.
• Experience working closely with QA/SDET teams.
• Strong understanding of Agile/Scrum methodologies.
• Strong technical problem-solving and dependency-management skills.
• Excellent communication and stakeholder-management skills. Preferred / Good to Have
• Experience working on AI-native SaaS platforms.
• Experience with Patent Intelligence, Legal Tech, Document Intelligence, Knowledge Management, or Search platforms.
• Experience handling large-scale structured/unstructured datasets.
• Experience with semantic or enterprise search systems.
• Experience working with AI-powered enterprise applications.
• Previous experience as a Software Engineer, Full-Stack Engineer, AI/ML Engineer, Data Engineer, Tech Lead, Solution Architect, or Engineering Lead.
• Hands-on coding or architecture experience.
• Experience taking products from requirements → development → testing → deployment → production. Technical Skills Candidates should have strong working knowledge of several of the following: AI / GenAI
• LLMs
• Generative AI
• RAG
• AI Agents / Agentic AI
• NLP
• Embeddings
• Vector Databases
• Semantic Search
• AI Evaluation Backend
• Node.js
• Express.js
• Python
• FastAPI
• REST APIs
• Microservices Frontend
• React.js
• Next.js
• TypeScript
• Redux Data
• SQL
• PostgreSQL / MySQL
• MongoDB
• ETL/ELT
• Data Pipelines
• Data Modelling
• Data Quality
• Search Indexing Search
• Elasticsearch
• OpenSearch
• Vector Search
• Semantic / Hybrid Search Cloud & DevOps
• AWS
• Docker
• CI/CD
• Git Project Management
• Jira
• Confluence
• Agile/Scrum
• Sprint Planning
• Release Management
• Risk & Dependency Management Ideal Candidate Profile We are looking for someone who has grown from a strong technical/engineering background into a Technical Project Management or technical delivery role. The ideal candidate should be capable of having meaningful technical discussions with: AI Engineers about LLM/RAG/Agents, Data Engineers about data pipelines and data architecture, Full-Stack Engineers about APIs, application architecture and integrations, and QA/SDET Engineers about automation, API testing and AI quality. The candidate should be able to understand a technical problem, identify dependencies, coordinate the right teams, drive resolution, and ensure successful delivery. What Success Looks Like In this role, you will be successful when you can:
• Convert complex product requirements into clear technical execution plans.
• Keep AI, Data, Full-Stack, and QA teams aligned.
• Identify technical dependencies before they become blockers.
• Drive timely resolution of technical issues.
• Maintain engineering quality while meeting delivery timelines.
• Communicate technical challenges clearly to management.
• Drive projects from planning through production delivery.
• Build an efficient and accountable engineering delivery process.