About the Job
Mastercard is hiring for the role of AI Engineer as part of its AI & Data team. The role will involve building AI and Generative AI solutions that solve business problems, enhance processes and drive decision-making.
The role will require working with technologies such as LLMs, Generative AI, Agentic AI, RAG architectures and cloud AI platforms. Candidates will also work on AI/ML pipelines, cloud integrations, deployment, monitoring and Responsible AI practices. As an AI Engineer, you will be part of Mastercard’s Global Business Services Center (GBSC) Analytics and Automation team. The team works on data analysis, automation and new-age technologies to generate actionable insights and improve business operations. The role is suitable for candidates who have a strong technical background and are interested in building and deploying practical AI applications. Hands-on experience with production-level AI applications is preferred for this position.
Job Overview
Eligibility Criteria
- Candidates should have a Bachelor’s degree in Computer Science, Data Science, AI/ML or a related technical field.
- Hands-on experience developing and successfully deploying production-level AI applications is preferred.
- Candidates should have experience with Python, Spark or SQL.
- A basic understanding of LLMs, Generative AI concepts, prompt engineering and AI/ML workflows is expected.
- Exposure to at least one cloud platform or AI ecosystem such as Azure, AWS, Databricks, Microsoft Fabric or Microsoft Copilot technologies is preferred.
- Familiarity with Docker or Kubernetes is preferred.
- Familiarity with APIs, data pipelines, ETL processes or cloud-based integrations is useful.
- Exposure to AI/ML frameworks, vector databases or orchestration frameworks such as LangChain is a plus.
- Understanding of software development lifecycle, deployment processes and version control tools such as Git is preferred.
- Strong analytical, problem-solving and communication skills are required.
- Candidates should have an eagerness to learn emerging AI technologies and work in a fast-evolving AI engineering environment.
- The job description does not specify a particular graduation year or fixed experience range.
Key Responsibilities
- Design, develop and implement AI and Generative AI solutions using LLMs, Agentic AI frameworks, RAG architectures and cloud AI platforms.
- Develop and maintain scalable AI/ML pipelines, data preparation workflows and cloud-native integrations using platforms such as Azure, Databricks and AWS.
- Assist in integrating AI capabilities with APIs, databases, cloud services and internal applications.
- Support deployment, testing, monitoring and operational maintenance of AI/ML solutions following MLOps and LLMOps best practices.
- Implement Responsible AI and AI governance practices including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails and compliance with data ethics standards.
- Stay updated on emerging trends and technologies related to Generative AI, LLMs, Agentic AI and cloud platforms.
Skills Required
- Programming: Python, Spark, SQL
- Generative AI: LLMs, Generative AI, Prompt Engineering
- AI Architecture: Agentic AI, RAG architectures
- Cloud: Azure, AWS, Databricks, Microsoft Fabric, Microsoft Copilot technologies
- AI Engineering: AI/ML Pipelines, MLOps, LLMOps
- Development: APIs, Git, Docker, Kubernetes
- Data: ETL, Data Pipelines, Databases
- AI Frameworks: LangChain, Vector Databases, AI/ML frameworks
- Responsible AI: AI Governance, Bias Detection, Hallucination Mitigation, Explainability, Safety Guardrails
- Soft Skills: Analytical Thinking, Problem Solving, Communication, Willingness to Learn
Skills to Add in Your Resume
| Programming | Python, SQL, Spark |
| Generative AI | LLMs, Generative AI, Prompt Engineering |
| AI Architecture | RAG, Agentic AI |
| Cloud | Azure, AWS, Databricks, Microsoft Fabric |
| AI Engineering | AI/ML Pipelines, MLOps, LLMOps |
| Development | APIs, Git, Docker, Kubernetes |
| Data & Frameworks | ETL, Data Pipelines, Databases, LangChain, Vector Databases |
| Responsible AI | AI Governance, Safety Guardrails, Explainability |
Benefits
- The provided job description does not mention specific employee benefits or compensation.
- However, the role offers exposure to several areas of modern AI engineering, including Generative AI, LLMs, Agentic AI, RAG, cloud AI platforms, MLOps and Responsible AI.
- It can also provide practical experience in developing AI solutions, integrating them with existing systems and supporting their deployment and monitoring.
Who Can Apply?
- Candidates with a Bachelor’s degree in Computer Science, Data Science, AI/ML or a related technical field can consider applying if they meet the technical requirements mentioned in the job description.
- The position is particularly relevant for candidates who have hands-on experience with AI applications and are comfortable working with technologies such as Python, SQL/Spark, cloud platforms, LLMs and AI/ML workflows.
- The job description does not mention a specific passout year or say that the position is exclusively for freshers.
How to Apply
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