Part Time Ai Manager Jobs Near Me

As a Senior Technical Product Manager, you will define, build, and scale AI-powered products, tools, workflows, and decision-support systems that drive measurable business impact.

A successful product manager will identify opportunities where artificial intelligence can improve productivity, automate repetitive work, enhance decision making, and unlock new capabilities across the organization. This role will be responsible for translating business challenges into a portfolio of high-value AI initiatives, ranging from rapid experiments and process improvements to transformational products and platforms.

This position goes beyond traditional product management. The ideal candidate combines strategic product thinking with hands-on technical execution, actively building prototypes, evaluating emerging AI technologies, testing models and APIs, and guiding successful solutions from concept through deployment and adoption.

• Bachelor's degree in Computer Science, Engineering, Information Systems, Product Management, or a related field
• 5+ years of experience in product management, software engineering, or technical program management
• 2+ years of experience delivering AI-powered products, automation solutions, workflow improvements, or internal platforms from concept through deployment.
• Hands-on experience working with AI technologies such as LLM APIs, multimodal models, agent frameworks, prompt engineering, retrieval systems, workflow automation platforms, and orchestration tools.
• Strong understanding of product management fundamentals, including discovery, prioritization, roadmap planning, experimentation, and outcome measurement.
• Excellent communication, presentation, and stakeholder management skills.
• Ability to thrive in ambiguous, rapidly evolving technology environments.

• Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related discipline.
• Experience building AI workflows using platforms such as OpenAI, Anthropic, LangChain, LangGraph, Python-based systems, automation platforms, or similar technologies.
• Experience launching enterprise AI applications, copilots, agentic workflows, or intelligent automation solutions.
• Familiarity with responsible AI frameworks, model evaluation methodologies, governance practices, and AI risk management.
• Experience assessing and implementing third-party AI products, platforms, and vendor solutions.

• Own and drive the AI product roadmap from concept to production, balancing experimentation, speed, scalability, and long-term business value.
• Identify workflow inefficiencies, operational bottlenecks, and emerging opportunities where AI can deliver measurable improvements.
• Evaluate opportunities based on impact, feasibility, risk, and scalability, creating a prioritized portfolio of initiatives.
• Define product requirements, success metrics, adoption goals, and implementation strategies for AI-powered products and solutions.
• Lead initiatives through discovery, prototyping, pilot programs, full-scale deployment, and continuous optimization.

• Stay current with advancements in artificial intelligence, machine learning, large language models (LLMs), agent-based systems, multimodal AI, and workflow automation technologies.
• Develop prototypes, automations, internal tools, and proof-of-concepts to validate ideas and accelerate learning.
• Work hands-on with modern AI technologies, including LLM APIs, orchestration frameworks, agent platforms, evaluation systems, and automation tools.
• Rapidly test, measure, and iterate on solutions with a focus on delivering tangible business outcomes.
• Partner with engineering teams and external vendors to scale successful prototypes into production-ready products and platforms.
• Assess build-versus-buy decisions and recommend the optimal approach for delivering strategic capabilities.

• Collaborate with stakeholders across product, engineering, design, operations, customer-facing teams, and business functions to identify and prioritize AI opportunities.
• Build alignment around objectives, roadmaps, priorities, and success measures.
• Translate complex technical concepts into clear business recommendations and execution plans.
• Create repeatable frameworks, playbooks, training materials, and best practices that drive adoption and organizational AI maturity.
• Support vendor evaluation, integration, and partnership strategies to accelerate innovation and capability development.

• Identify and manage risks related to AI adoption, including data quality, model accuracy, security, privacy, compliance, bias, and over-reliance on automation.
• Establish governance practices and appropriate safeguards to ensure responsible and effective deployment of AI solutions.
• Define and track key performance indicators across initiatives, including productivity gains, efficiency improvements, quality enhancements, cost savings, and business outcomes.
• Communicate progress, learnings, and portfolio impact to stakeholders and leadership.