AI - ML - Remote opportunity

Company Description

• WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees.

Job Description

• Min Exp - 5-8  years
• Location - Remote
• Shift timings - 6 pm to 3 am (Night Shift)
• Exp with Data Drift is important

Engagement & Project Overview

An AI model trainer brings specialised knowledge in developing and fine-tuning machine learning models. They can ensure that your models are accurate, efficient, and tailored to your specific needs. Hiring an AI model trainer and tester can significantly enhance our data management and analytics capabilities

Job Description

1. Expertise in Model Development:

• Develop and fine-tune machine learning models.
• Ensure models are accurate, efficient, and tailored to our specific needs.

     2. Quality Assurance:

• Rigorously evaluate models to identify and rectify errors.
• Maintain the integrity of our data-driven decisions through high performance and reliability.

     3. Efficiency and Scalability:

• Streamline processes to reduce time-to-market.
• Scale AI initiatives and ML engineering skills effectively with dedicated model training and testing.

4. Production ML Monitoring & MLOps:

• Implement and maintain model monitoring pipelines to detect data drift, concept drift, and model performance degradation.
• Set up alerting and logging systems using tools such as Evidently AI, WhyLabs/Prometheus + Grafana or cloud-native solutions (AWS SageMaker Monitor, GCP Vertex AI, Azure Monitor).
• Collaborate with teams to integrate monitoring into CI/CD pipelines, using platforms like Kubeflow, MLflow, Airflow, and Neptune.ai.
• Define and manage automated retraining triggers and model versioning strategies.
• Ensure observability and traceability across the ML lifecycle in production environments.

Qualifications

Qualifications:

• 5+ years of experience in the respective field.
• Proven experience in developing and fine-tuning machine learning models.
• Strong background in quality assurance and model testing.
• Ability to streamline processes and scale AI initiatives.
• Innovative mindset with a keen understanding of industry trends.
• License/Certification/Registration