Technical Product Manager

Technical Product Manager 

1. Product Strategy and Vision Define Product Roadmap: Develop a long-term product strategy for AI agents, aligning with client needs and industry trends. Market Research: Conduct research on emerging AI technologies, customer pain points, and competitor products to identify opportunities and guide product features. Client Interaction: Regularly engage with clients to understand their goals, challenges, and requirements, ensuring the AI agent product meets those needs. 
2. Requirements Gathering and Prioritization Gather Functional and Technical Requirements: Collaborate with clients, stakeholders, and AI experts to gather clear, actionable requirements for AI agents. Prioritize Features: Work with cross-functional teams (engineering, data science, etc.) to prioritize features based on business value, technical feasibility, and client impact. 
3. Cross-functional Collaboration Work with Engineering Teams: Partner closely with developers, data scientists, and AI engineers to ensure technical alignment and the feasibility of product features. Collaborate with UX/UI Teams: Ensure the design and user experience are optimal for clients and end-users of AI agents. Coordinate with Sales and Marketing: Provide technical expertise and support for go-to-market strategies, client demos, and product launches. 
4. Product Development and Lifecycle Management Oversee Development Process: Guide the development lifecycle for AI agents, from ideation to deployment, ensuring the product stays on track in terms of scope, budget, and timelines. Agile Management: Facilitate Agile processes (e.g., sprint planning, backlog grooming, and reviews) to ensure continuous progress and adaptability during development cycles. Product Testing: Collaborate with QA teams to ensure thorough testing of AI agents, including functionality, performance, and scalability. Metrics: Define and track success metrics such as adoption rates, client satisfaction, performance benchmarks, and return on investment (ROI). Communications: Regularly communicate product status, performance, and business outcomes to internal stakeholders, executives, and clients. 
5. Data and AI Model Management Data Strategy: Define strategies for gathering, cleaning, and managing the data needed to train and improve AI models. AI Model Optimization: Work with AI researchers and engineers to ensure the continuous improvement and tuning of the models that power the agents. Monitor AI Performance: Track the effectiveness of AI agents, using data and feedback to optimize their behavior, accuracy, and client value. 
6. Integration and Managed Services Lead integrations such as AI Agent integrations with ERP systems, ServiceNow, Agent integration with related Client Systems. Demonstrate expertise in Run/Operate/Support construct for our Managed Services businesses.  
7. Client Engagement and Support Provide Technical Expertise: Serve as a bridge between technical teams and clients, explaining AI capabilities and constraints clearly and translating client needs into technical specifications. Client Training and Onboarding: Ensure clients understand how to deploy, use, and optimize AI agents. Post-Launch Support: Monitor and manage ongoing support and feature updates after product release, ensuring continuous improvement and client satisfaction. 
8. Compliance and Ethical AI Ensure Compliance: Ensure that the build/ delivery approach demonstrably incorporates adherence to Client InfoSec Policies & Standards. Additionally, ensure compliance with any applicable industry regulations and legal standards, particularly around data privacy (e.g., GDPR, CCPA). Promote Ethical AI: Ensure that the AI agents are designed and implemented ethically, minimizing bias and ensuring fairness in decision-making. This role requires both technical expertise and strong product management skills to bridge the gap between clients, engineering, and AI capabilities.