Overview (Our Team)

- As consumer preference for digital payments continues to grow, ensuring a seamless and secure consumer experience is top of mind. The Optimization Solutions team focuses on tracking digital performance across products and regions, understanding factors influencing performance and the broader industry landscape, and delivering data-driven insights and recommendations. We engage directly with key stakeholders to implement optimization solutions (new and existing) and partner across the organization to drive alignment and action.
- If youre excited about data assets, passionate about data-driven decision-making, and want to build large-scale analytical capabilities used across global markets, this is the role for you.

The Role

- As a Senior AI Engineer, you will architect, develop, and deploy AI/ML solutions that generate actionable insights for product optimization and sales enablement. You will work with global stakeholders across geographies, develop reusable and scalable models, and partner with engineering teams to productionize AI capabilities.
- This role emphasizes end-to-end ownership: problem definition data feature pipelines modelling evaluation deployment monitoring iterationwith strong attention to governance, privacy, and operational excellence aligned to Mastercard standards.

- Key Responsibilities
- AI/ML Solution Development Innovation
- Architect, build, and maintain AI/ML systems to solve business problems, including predictive modelling and decisioning solutions for optimization use cases
- Prototype new algorithms, run experiments, evaluate performance against agreed metrics, and deliver production-ready insights and models
- Translate ambiguous business challenges into measurable ML objectives; simplify complex technical requirements to align with stakeholder needs.
- Data Engineering Foundations for AI
- Perform data ingestion,



aggregation, processing, and feature engineering on high-volume, high-dimensional datasets to enable reliable training and inference
- Apply benchmarking, measurement, and metric design to validate model impact and support decision-making.
- Reusable Scalable AI (Patterns + Microservices)
- Identify common use-case patterns and promote scalable AI delivery via reusable models, shared components, and a microservice approach.
- Drive the evolution of AI-enabled products by improving model robustness, latency, and maintainability.
- Productionization, MLOps Operational Excellence
- Deploy models into production in partnership with technical teams; design scalable training/inference pipelines and deployment frameworks
- Automate training, testing, deployment, and updates using CI/CD best practices; manage model versioning and performance monitoring (drift, quality, reliability
- Governance, Privacy Responsible AI
- Ensure AI solutions follow industry standards and Mastercard practices for data management and privacycovering data collection, storage, access, retention, outputs/reporting, and quality.
- Contribute to ethical AI practices and robust AI infrastructure to support reliable production operations
- Collaboration Leadership
- Collaborate with global stakeholders to gather information, define business problems, and deliver outcomes across teams and geographies.
- Mentor and guide junior team members, fostering a culture of learning and continuous improvement.





- All About You (Required Qualifications)
- 5+ years of experience in Data Science / AI / Machine Learning, including strategy, execution, and solution development from the ground up.
- Strong hands-on expertise with:
- o Python (preferred), R, and SQL; proficiency with statistical and ML development workflows.
- o Classical ML methods (e.g., Logistic Regression, Decision Trees, K-Means, PCA, Time Series models such as ARIMA/ARMA).
- o Advanced ML / DL approaches (e.g., Gradient Boosting/GBM, Neural Networks including CNN/LSTM; optimization methods such as Adam/Adagrad).
- o Production frameworks: TensorFlow, Keras, PyTorch, XGBoost.
- Experience working with big data and scalable compute (e.g., Hadoop/Hive/Spark, GPU-enabled environments).
- Demonstrated practical AI mindset: ability to simplify complexity, make tradeoffs, and deliver business-aligned outcomes.
- Excellent written and verbal communication skills; ability to influence and partner across disciplines.
- Computer Science (or closely related) background.

- Effectiveness / What Success Looks Like
- Strong problem-solving: break down complex problems, select the right AI techniques, and deliver confidently validated solutions.
- Ability to manage assumptions and validate them with stakeholders under tight deadlines while keeping delivery on track.
- Deep attention to detail and a high bar for quality, reproducibility, and operational reliability.
- Strong architectural thinking: anticipate system interdependencies, constraints, and production challenges proactively.

- Core Capabilities
- Clear communicator who can bridge technical and non-technical audiences.
- Robust project management and stakeholder management skills.
- Team-first mindset; effective in global, cross-functional collaboration.

📌 Senior AI Engineer (Haryana)
🏢 Mastercard
📍 Haryana