AI and ML Manager

About the Company : Our client is an advanced AI research and product company focused on building intelligent systems that combine deep reasoning, natural language understanding, and adaptive learning. Its mission is to develop technologies that can seamlessly assist individuals and enterprises in decision-making, creativity, and automation.

The company operates at the intersection of machine learning, large language models, and cognitive computing, creating scalable AI solutions that enhance productivity and unlock new forms of human–machine collaboration.

Key Responsibilities :

Lead end-to-end AI / ML solutioning — from problem framing and data preparation to model development, deployment, and monitoring.

Partner with business and product teams to identify high-impact AI / ML use cases within Financial Services (BFSI, Fintech, Payments, Wealth Management).

Build and productionize scalable ML models and pipelines using Python , Scikit-learn , TensorFlow , or PyTorch.

Drive feature engineering, model evaluation, and performance optimization aligned to business KPIs.

Deploy ML solutions on cloud platforms (AWS / Azure / GCP) using MLOps frameworks and CI / CD best practices.

Collaborate with data engineering teams to ensure high-quality, reliable datasets.

Translate technical insights into business-friendly narratives and present findings to CXOs and senior stakeholders.

Mentor and guide junior data scientists and ML engineers , fostering a culture of learning and innovation.

Stay ahead of emerging trends in Generative AI , LLMs , and Agentic AI , and evaluate their applicability to business problems.

Experience and Qualifications Required :

4–7 years of overall experience with 3+ years in applied Machine Learning.

Proven track record in delivering AI / ML projects with measurable business outcomes.

Strong proficiency in Python and key ML libraries ( Scikit-learn, TensorFlow, PyTorch ).

Hands-on experience deploying models on AWS , Azure , or GCP.

Familiarity with data pipelines , APIs , and cloud-native architectures.

Exposure to BFSI use cases such as credit risk modeling, fraud detection, churn prediction, customer segmentation , or marketing analytics.

Strong foundation in statistics, probability, and linear algebra.

Excellent communication, presentation, and stakeholder management skills.

Educational background in Engineering, Computer Science, Mathematics, Statistics , or related fields (MBA or Master’s in Data Science preferred).

Curiosity for AI innovation , experimentation, and emerging technologies.