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.