hybrid
Senior Specialist Client Services Project Manager (AI)
Senior Specialist Client Services Project Manager (AI)
The Senior Specialist Client Services Project Manager (AI) will oversee multiple projects, with a focus on high-visibility software and AI/GenAI initiatives. This role involves managing project lifecycles, ensuring on-time delivery, maintaining communication with executive leadership, and mentoring other project managers.
About the role
About the Role
As a Senior Specialist Client Services Project Manager, you will be responsible for overseeing multiple projects and initiatives that support the organization's strategic goals. You will work closely with cross-functional teams to ensure successful project execution, on-time delivery, and adherence to quality standards.
Responsibilities
- Manage high‑visibility software and AI/GenAI projects, ensuring alignment with scope, timelines, and customer expectations.
- Manage project plans, schedules, RAID logs, and core project documentation.
- Provide consistent communication with executive leadership.
- Drive ownership, accountability, and escalation management.
- Guide teams through the full project lifecycle.
- Mentor project managers and support PMO best‑practice maturity.
Requirements
- IT experience: 12 to 16 years.
- 7 years of project management experience leading multiple concurrent projects in customer‑facing environments.
- Proven success delivering full lifecycle projects across Agile, Waterfall, and hybrid approaches.
- Experience delivering technical, SaaS, and AI/GenAI programs.
- Strong analytical skills with a track record of managing risks, revenue, and escalations.
- Excellent communication and expectation‑management skills.
- Advanced proficiency with tools such as MS Project, Salesforce, Rocketlane, and Power BI.
- PMP certification required.
Preferred AI Delivery Experience
- Led end‑to‑end delivery of conversational AI, LLM, and NLU solutions across scoping, execution, UAT, and deployment.
- Assessed AI use cases and evaluated data readiness, including ingestion, quality, labeling, and dependency tracking.
- Collaborated with engineering, product, R&D, and presales teams to validate model accuracy, output quality, and overall performance.
- Set and managed customer expectations while coordinating cross‑functional delivery across technical and business teams.
- Identified and mitigated AI‑specific risks such as model drift, hallucinations, data issues, and expectation misalignment.
- Applied hands‑on prompt engineering with a clear understanding of prompting limitations and optimization strategies.
- Leveraged AI tools in daily workflows for automation, data analysis, content generation, and quality validation.
- Defined and monitored AI performance KPIs, translating metrics (e.g., accuracy, containment, hallucination rate, latency, cost per inference) into actionable insights.
- Managed AI cost drivers, e.g. token usage, model selection, prompt structure, and architecture optimization for efficiency and performance.
- Ensured compliance with AI safety, governance, and security standards, implementing guardrails, redaction practices, and structured evaluation frameworks.
Additional Advantages
- Experience working with Waterfall/Agile methodologies.
- Experience with Onsite and offshore delivery model.