Assistant Vice President – AI Model Operations Practice

At Genpact, we don’t just adapt to change—we drive it. AI and digital innovation are redefining industries, and we’re leading the charge. Genpact’s AI Gigafactory, our industry-first accelerator, is an example of how we’re scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform m large-scale models to agentic AI, our breakthrough solutions tackle companies’ most complex challenges.

If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that’s shaping the future, this is your moment.

Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at  and on Linked In, X, You Tube, and Facebook.

Inviting applications for the role of  Assistant Vice President – AI Model Operations Practice

This leader will play a central role in shaping how large-scale AI and ML ecosystems operate, learn, and improve. This role demands someone who can deeply understand the products and models we support, how human-in-the-loop workflows impact model quality, and how operational data feeds into model evolution. He/she will help grow our practice in this field (which includes Data labeling / Annotation, Generative AI Operations, Responsible AI practices) through research and insights, identifying new growth areas, drive new deals and bring new business and thought-leadership for existing clients

Responsibilities
Research & Analysis:
Conduct in-depth research to identify emerging AI Model Operations risks, trends, regulations, and distinct industry specific vulnerabilities. Analyse complex information, data sets to understand patterns and root causes of issues.
Capability Development:
Design and implement innovative solutions to drive AI Model Operations. This includes developing new policies, processes, tools, and technologies. Ability to ideate and create first time solutions to evolving challenges.

Cross-functional Collaboration:

Work closely with various teams across the organization, including engineering, product, legal, consulting, and risk, to build holistic and effective AI Model Operations measures.
External Partnerships:
Build and maintain relationships with external stakeholders, such as industry partners, civic society, academia, and crowd sourcing players, to stay ahead of emerging trends and derive best practices.
Scalability:
Develop scalable AI Model Operations solutions that can adapt to platforms and challenges of various dimensions

• Thought Leadership:
Represent the company as a thought leader in the AI Model Operations space through presentations, publications, and industry engagement.
Qualifications we seek in you!

Minimum Qualifications / Skills

• Proven track record in AI Model Operations roles, with varied experience in scaled managed operations and crowdsourced work

• Deep understanding of AI Model Operations technologies and best practices.

• Experience with data analysis and research methodologies.

• Demonstrated ability to collaborate effectively across diverse teams and functions.

• Strong project management skills and the ability to deliver results in a fast-paced environment.

• Excellent communication and presentation skills.

Preferred Qualifications / Skills

• Strong understanding of AI operations, human-in-the-loop systems, annotation strategies, and supervised learning workflows.

• Familiarity with ML concepts including training data pipelines, ground-truth quality, feature labeling, taxonomies, keyword hierarchies, and model evaluation metrics.

• Experience working with ML/AI product, engineering, or data science teams to refine guidelines, requirements, or annotation strategies.

• Knowledge of labeling tools, annotation platforms, quality scoring frameworks, sampling methodologies, and reviewer decisioning systems.

• Strong analytical…