Senior Software Engineer-2
Mastercard · Pune · Posted 2026-07-28
Tech stack: Java, Python
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About the role
The MasterCard Business Intelligence application development team is engaged in working with new and innovative technologies to build business solutions that keep MasterCard positioned as a leader in delivering value added business analytic and reporting solutions to our diverse customer base. We work collaboratively with our product partners and other technical teams to continuously improve and enhance our existing products and drive new products to the global marketplace.
We are looking for a Senior AI Java Engineer (Hands-on IC) to deliver AI-driven capabilities across our roadmap. This is a builder
Responsibilities:
- you will write code daily, own critical components end-to-end, and ship production-grade AI-enabled services with strong operability, security, scalability, and resiliency.
- This role blends Software Engineering (deterministic systems, reliable services) with AI Engineering (probabilistic systems, model lifecycle, monitoring and improvement over time).
- Role (Essential Responsibilities) (5–7 maximum)
- In this role, you will:
- Build and own Java services/APIs that deliver AI-powered features, ensuring functional correctness, performance, and maintainability in a multi-tier, distributed environment.
- Develop hands-on AI components in Python and productionise them, contributing to model workflows that support training/tuning and/or inference use cases.
- Implement and operate AI systems in production, including deployment frameworks, automation for training/testing/deploy/update, and model versioning/monitoring to sustain high-quality outputs over time.
- Own delivery across the lifecycle: estimate and deliver design/dev/test/deploy/config/documentation work; automate build and run aspects of software.
- Drive engineering excellence through code/test/automation reviews, adoption of standards and best practices, and pragmatic design trade-offs within the team.
- Troubleshoot complex issues spanning services and AI components, applying strong problem-solving, triage, and root-cause discipline.
- Mentor engineers via technical guidance and hands-on reviews (without people-management responsibilities), raising the bar on quality, operability, and AI readiness.
- All About You (Must-Have Skills)
- The ideal candidate demonstrates advanced, hands-on expertise across the areas below. (Per template guidance: do not include years-of-experience; focus on proficiency.)
- Strict Must-Haves (Non-negotiable)
- Python – MUST: proven hands-on Python engineering for AI workloads (writing production code, tests, packaging, and operationalising components).
- AI Frameworks – MUST: hands-on experience with one or more modern AI frameworks (e.g., PyTorch / TensorFlow / Hugging Face) used for building, tuning, or serving models.
- Core Engineering Must-Haves
- Strong Java engineering (designing, coding, testing, maintaining software; building scalable and efficient solutions; debugging complex issues).
- Proven ability to deliver scalable, reliable software with strong testing discipline (unit/integration/other testing mechanisms) and operational mindset.
- Hands-on experience with production AI/ML lifecycle elements such as model deployment, automation of workflows, and monitoring performance over time.
- Strong communication and precision in technical discussions; ability to collaborate effectively across roles and geographies.
- All About You (Nice-to-Have / Preferred)
- (Keep preferred skills limited; not required for success but helpful.)
- Experience building AI systems that include data ingestion/pre-processing/feature engineering workflows supporting training and inference.
- Experience with CI/CD practices applied to AI delivery (automating build/test/deploy/update) and strong observability practices.
- Experience improving AI system robustness through production safeguards (e.g., monitoring, evaluation, and operational controls aligned to business requirements).
Qualifications:
- All About You (Must-Have Skills)
- The ideal candidate demonstrates advanced, hands-on expertise across the areas below. (Per template guidance: do not include years-of-experience; focus on proficiency.)
- Strict Must-Haves (Non-negotiable)
- Python – MUST: proven hands-on Python engineering for AI workloads (writing production code, tests, packaging, and operationalising components).
- AI Frameworks – MUST: hands-on experience with one or more modern AI frameworks (e.g., PyTorch / TensorFlow / Hugging Face) used for building, tuning, or serving models.
- Core Engineering Must-Haves
- Strong Java engineering (designing, coding, testing, maintaining software; building scalable and efficient solutions; debugging complex issues).
- Proven ability to deliver scalable, reliable software with strong testing discipline (unit/integration/other testing mechanisms) and operational mindset.
- Hands-on experience with production AI/ML lifecycle elements such as model deployment, automation of workflows, and monitoring performance over time.
- Strong communication and precision in technical discussions; ability to collaborate effectively across roles and geographies.
- All About You (Nice-to-Have / Preferred)
- (Keep preferred skills limited; not required for success but helpful.)
- Experience building AI systems that include data ingestion/pre-processing/feature engineering workflows supporting training and inference.
- Experience with CI/CD practices applied to AI delivery (automating build/test/deploy/update) and strong observability practices.
- Experience improving AI system robustness through production safeguards (e.g., monitoring, evaluation, and operational controls aligned to business requirements).
Qualifications
- All About You (Must-Have Skills)
- The ideal candidate demonstrates advanced, hands-on expertise across the areas below. (Per template guidance: do not include years-of-experience; focus on proficiency.)
- Strict Must-Haves (Non-negotiable)
- Python MUST: proven hands-on Python engineering for AI workloads (writing production code, tests, packaging, and operationalising components).
- AI Frameworks MUST: hands-on experience with one or more modern AI frameworks (e.g., PyTorch / TensorFlow / Hugging Face) used for building, tuning, or serving models.
- Core Engineering Must-Haves
- Strong Java engineering (designing, coding, testing, maintaining software; building scalable and efficient solutions; debugging complex issues).
- Proven ability to deliver scalable, reliable software with strong testing discipline (unit/integration/other testing mechanisms) and operational mindset.
- Hands-on experience with production AI/ML lifecycle elements such as model deployment, automation of workflows, and monitoring performance over time.
- Strong communication and precision in technical discussions; ability to collaborate effectively across roles and geographies.
- All About You (Nice-to-Have / Preferred)
- (Keep preferred skills limited; not required for success but helpful.)
- Experience building AI systems that include data ingestion/pre-processing/feature engineering workflows supporting training and inference.
- Experience with CI/CD practices applied to AI delivery (automating build/test/deploy/update) and strong observability practices.
- Experience improving AI system robustness through production safeguards (e.g., monitoring, evaluation, and operational controls aligned to business requirements).
Responsibilities
- you will write code daily, own critical components end-to-end, and ship production-grade AI-enabled services with strong operability, security, scalability, and resiliency.
- This role blends Software Engineering (deterministic systems, reliable services) with AI Engineering (probabilistic systems, model lifecycle, monitoring and improvement over time).
- Role (Essential Responsibilities) (5–7 maximum)
- In this role, you will:
- Build and own Java services/APIs that deliver AI-powered features, ensuring functional correctness, performance, and maintainability in a multi-tier, distributed environment.
- Develop hands-on AI components in Python and productionise them
- contributing to model workflows that support training/tuning and/or inference use cases.
- Implement and operate AI systems in production, including deployment frameworks, automation for training/testing/deploy/update, and model versioning/monitoring to sustain high-quality outputs over time.
- Own delivery across the lifecycle: estimate and deliver design/dev/test/deploy/config/documentation work; automate build and run aspects of software.
- Drive engineering excellence through code/test/automation reviews, adoption of standards and best practices, and pragmatic design trade-offs within the team.
- Troubleshoot complex issues spanning services and AI components, applying strong problem-solving, triage, and root-cause discipline.
- Mentor engineers via technical guidance and hands-on reviews (without people-management responsibilities), raising the bar on quality, operability, and AI readiness.
- All About You (Must-Have Skills)
- The ideal candidate demonstrates advanced, hands-on expertise across the areas below. (Per template guidance: do not include years-of-experience; focus on proficiency.)
- Strict Must-Haves (Non-negotiable)
- Python MUST: proven hands-on Python engineering for AI workloads (writing production code, tests, packaging, and operationalising components).
- AI Frameworks MUST: hands-on experience with one or more modern AI frameworks (e.g., PyTorch / TensorFlow / Hugging Face) used for building, tuning, or serving models.
- Core Engineering Must-Haves
- Strong Java engineering (designing, coding, testing, maintaining software; building scalable and efficient solutions; debugging complex issues).
- Proven ability to deliver scalable, reliable software with strong testing discipline (unit/integration/other testing mechanisms) and operational mindset.
- Hands-on experience with production AI/ML lifecycle elements such as model deployment, automation of workflows, and monitoring performance over time.
- Strong communication and precision in technical discussions; ability to collaborate effectively across roles and geographies.
- All About You (Nice-to-Have / Preferred)
- (Keep preferred skills limited; not required for success but helpful.)
- Experience building AI systems that include data ingestion/pre-processing/feature engineering workflows supporting training and inference.
- Experience with CI/CD practices applied to AI delivery (automating build/test/deploy/update) and strong observability practices.
- Experience improving AI system robustness through production safeguards (e.g., monitoring, evaluation, and operational controls aligned to business requirements).