ML/AI Engineer II-2

Mastercard

5 - 8 years

14 - 19 Lacs

pune

Posted:13 hours ago| Platform:

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Skills Required

ai engineer data analysis forecasting var ai llm machine learning data engineering deep learning diagnostics automation use cases stakeholder management compliance regression feature engineering statistical modeling clustering ml

Work Mode

Work from Office

Job Type

Full Time

Job Description

Title and Summary

ML/AI Engineer II-2

Overview

Mastercards Business & Market Insights (B&MI) group empowers organizations to achieve growth & innovation goals by providing unparalleled data-driven insights and advanced analytics. By leveraging proprietary data and global expertise, B&MI helps businesses make smarter, more informed decisions that drive profitability and success. We turn complex data into actionable strategies that lead to better outcomes and sustained competitive advantage.
We are currently looking for a Senior Engineer, Machine Learning Engineering for Operational Intelligence Program, within B&MI group. This role would entail development and delivery of secure, scalable, and high-performing AI/ML solutions. As a senior technologist, this role will also focus on engineering best practices, next gen innovation and stakeholder management, while fostering a culture of continuous learning and technical excellence within the team.

Roles and Responsibilities:

Lead the design and development of AI and analytics solutions spanning classical machine learning, time-series forecasting, statistical modeling, deep learning, and emerging agent-based architectures.
Develop predictive and prescriptive models using supervised, unsupervised, and probabilistic approaches including regression, tree-based models, clustering, anomaly detection, Bayesian inference, and ensemble methods.
Build and optimize time-series forecasting frameworks leveraging ARIMA/SARIMA, ETS, Prophet, VAR models, state-space models, LSTM/GRU-based deep forecasting, and ML-based hybrid forecasting pipelines for financial and operational use cases.
Integrate Generative AI and multi-agent systems ( LangGraph , CrewAI , AutoGen ) with traditional ML and statistical methods to enable reasoning-driven automation, intelligent decision support, and domain-aware task execution.
Perform exploratory data analysis, feature engineering, and hypothesis-driven insights using statistical testing, experimental design, root-cause analysis, and uncertainty quantification to guide business-critical decisions.
Create reusable model components, frameworks, and evaluation workflows including model selection, hyperparameter tuning, cross-validation, drift detection, and benchmarking across classical ML and GenAI capabilities.
Ensure model governance, explainability , and responsible AI practices, using interpretability frameworks (SHAP, counterfactuals, partial dependence) along with fairness, transparency, and compliance standards.
Collaborate closely with business stakeholders, product teams, and data engineering partners to translate domain challenges into measurable analytical solutions with quantifiable benefits and ROI.
Monitor performance and continuously improve production models, leveraging statistical diagnostics, error decomposition, A/B experimentation, and closed-loop learning strategies.
Stay current with advances in machine learning, statistical modeling, deep learning, and agentic AI, evaluating emerging methods and incorporating them into future platform and capability roadmaps.

All About You:

Masters/bachelors degree in computer science or engineering, and a considerable work experience with a proven track-record of successfully building complex projects/products and delivering to aggressive market needs.
Advanced-level hands on experience designing, building and deploying both conventional AI/ML solutions and LLM/Agentic solutions.
Strong analytical and problem-solving abilities, with quick adaptation to new technologies, methodologies, and systems.
Strong applied knowledge and hands on experience in advanced statistical techniques, predictive modelling, machine learning algorithms, GenAI and deep learning frameworks..

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