AI Practice Lead
AI Practice Lead
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Job Description
AI Practice Lead
Job Description: AI practice lead will be supporting continuous advancement of AI tools and build in data feeding
models. Stay informed about emerging technologies that can enhance HIL processes such as AI RPA LLM etc.
Documentation and Training
Product lead is responsible for creating comprehensive SOPs & workflow along with process
maps training materials and other relevant documents
Provide guidance on best practices and
Conduct training sessions to keep the team informed on recent changes in Google Operations and
quality guidelines for AI.
Data Collection and Preparation: Work closely with the team to prepare data for training and evaluating
AI models. Identify and address any potential biases or imbalances in the data to prevent biased outcomes
from AI systems.
Risk Assessment and Mitigation Conduct risk assessments to identify any potential failures. Implement
FMEA and Poka Yoke for early detection of failure points and mistake proofing. Develop ethical
frameworks and governance for responsible AI.
Stakeholder Management:
Collaborate and Communicate with the team members including AI engineers VMO and
business stakeholders. Facilitate xfunctional collaboration to ensure that AI systems are aligned
with the needs and expectations of different stakeholders.
Actively participate in discussions and decisionmaking processes providing valuable insights and
expertise on AIrelated matters.
Technology Advancement:
Collaborate with the AI Eng team and other similar AI teams at Cognizant to implement best
practices.
Stay informed about industry trends emerging technologies and innovative approaches to AI
operations.
Key Skills and Qualifications:
AI Ops: 2 year experience in AI Ops
2 years of digital marketing experience and advanced understanding of industry leading digital advertising
tools such as Google Ads Google Marketing Platform (GMP) Amazon advertising Bing Ads Facebook
ads and others. (PREFERABLE)
Understanding of AI and ML includes knowledge of various algorithms techniques and their applications.
Data engineering proficiency: The ability to manage process and transform large datasets is crucial.
Cloud platforms expertise: Familiarity with cloud providers (AWS Azure GCP) and their AI/ML services.
(PREFERABLE)
MLOps knowledge: Understanding the lifecycle of ML models including development deployment
monitoring and retraining.
Ability to communicate clearly and concisely with all levels of external and internal stakeholders
Ownership and accountability of end to end customer experience
Ability to analyze complex problems develop creative solutions and make sound decisions
Problem solving ability to find alternate solutions for the customers
Analytical skills to monitor / analyze advertiser’s account
Self motivated and collaborative to get the job done
Employment Type
Full Time
Company Industry
Key Skills
- Practice Management
- Veterinary Experience
- Enterprise Architecture
- Office Manager Experience
- Enterprise Software
- Healthcare Management
- IaaS
- TOGAF
- Medical office experience
- Conflict Management
- COBIT
- Customer relationship management
