Top AWS Jobs in Pune

Lead architecture, design, development, and delivery of Mastercard’s cloud-native experimentation and analytics platform. Build scalable services, APIs, data pipelines, and analytical capabilities using AWS, Azure, Databricks, Spark, and modern engineering practices. Drive modernization, architecture reviews, technical strategy, cross-team execution, system reliability, security, observability, and continuous delivery while mentoring engineers and collaborating across globally distributed teams.

Leads and develops a high-performing software engineering team building full-stack business experimentation and optimization applications. Responsibilities include supervising and mentoring engineers, designing scalable service-oriented architectures, leading projects from inception through release, developing software, reviewing code, planning features, improving engineering processes, and collaborating across teams. The role requires expertise in full-stack, data, cloud, microservices, databases, and advanced software development practices.

Lead the design and development of scalable cloud-native data platforms and batch or real-time pipelines supporting AI, machine learning, and Generative AI. Build infrastructure for feature engineering, model training, inference, LLMs, RAG, embeddings, and vector databases. Ensure data quality, governance, security, observability, reliability, and cost efficiency while enabling MLOps and AgenticOps capabilities across enterprise AI initiatives.

Operate and improve multi‑cloud (AWS/Azure/hybrid) platforms with SRE practices: lead incident triage and RCA, manage Kubernetes and CI/CD pipelines, implement IaC and automation, enhance observability/AIOps, and drive DevSecOps and operational excellence across global 24x7 operations.

Design, build, and deploy AI and Generative AI solutions using LLMs, agent frameworks, and RAG. Develop scalable AI/ML pipelines, cloud-native integrations (Azure/Databricks/AWS), and MLOps/LLMOps deployments. Integrate AI with APIs, databases, and internal apps. Implement Responsible AI practices (bias detection, hallucination mitigation, explainability, safety guardrails). Monitor, test, and maintain production AI systems while staying current on Generative AI trends.

Collaborate with client-facing teams to gather technical requirements, design and implement GenAI and data solutions, write production-ready code and tests, follow architecture and coding best practices, use version control and CI/CD, and research/evaluate new technologies and prototypes.

Performs manual penetration testing of web, mobile, API, and microservices applications; conducts secure code reviews and design assessments; identifies and validates vulnerabilities; documents findings; supports remediation and incident response; guides junior testers; and promotes secure coding and application security awareness.

Design and develop a scalable automated locates platform for securities finance using Java, Spring, microservices, distributed systems, containers, AWS, SQL, caching, and messaging technologies. Build high-performance enterprise applications, optimize transactional data models and queries, integrate real-time processing systems, and collaborate with development, business analysis, QA, and operations teams.

Leads technical and functional analysis for enterprise Java applications, serving as the connection between business stakeholders and development teams. Gathers and translates requirements into specifications, user stories, workflows, APIs, and data mappings. Supports solution design, Agile ceremonies, QA, UAT, defect resolution, releases, and production validation. Collaborates with architects, developers, product owners, and business teams while maintaining governance documentation and identifying process improvements.

Design, build, and maintain scalable data pipelines and cloud data platforms to support AI/ML workloads. Deploy, monitor, and operationalize machine learning models, implement CI/CD and MLOps, ensure data quality, scalability, and governance while collaborating with data scientists, engineers, and stakeholders.

Manage AWS infrastructure across multiple regions and accounts, including Linux servers, serverless resources, and container infrastructure. Responsibilities include planning, configuration, migrations, upgrades, patching, monitoring, log analysis, performance reporting, access control, infrastructure audits, troubleshooting, root cause analysis, and threat mitigation. The role also involves mentoring team members and supporting secure, resilient cloud operations in a multi-domain environment.

Support data governance policy implementation, data discovery, classification, cataloging, quality monitoring, metadata and lineage documentation, risk assessments, audits, tool administration, and remediation. Partner with data owners, stewards, and IT teams to promote secure, compliant, and trusted data. Provide training, user support, workflow assistance, and governance dashboards for leadership.

Design and build a regional observability data lake for logs, metrics, traces, and events across cloud-native services. Develop distributed services, cloud platforms, CI/CD and infrastructure automation, while applying SRE principles for resiliency, supportability, and observability. Collaborate with product and engineering stakeholders, troubleshoot complex dependencies, contribute to architecture reviews, and promote security, standards, automation, mentoring, and developer productivity.

Build and deploy AI and machine learning solutions using distributed computing, cloud platforms, data pipelines, and production-grade software engineering practices. Responsibilities include technical requirements analysis, feature development, testing, code reviews, agile collaboration, architecture validation, and prototyping emerging technologies. The role partners with data scientists, architects, client-facing teams, and clients to deliver scalable AI solutions and manage machine learning lifecycles.

Collaborates with internal and client teams to analyze business problems, translate requirements into technical designs, and implement technology solutions. Develops technical features using appropriate methodologies, testing practices, and technology stacks. Supports project implementation planning, interprets business requirements, and contributes to small- and medium-scale client engagements involving databases, programming, cloud platforms, and analytics.

Leads the architecture, design, and delivery of secure, scalable, resilient infrastructure across data center, hybrid cloud, and distributed environments. Partners with engineering, security, networking, cloud, product, and program teams to deliver modernization initiatives, automation, Infrastructure-as-Code, platform standardization, and operational excellence. Develops architecture standards and roadmaps while ensuring availability, compliance, disaster recovery, performance, and security for mission-critical platforms.

Develops and maintains data engineering solutions involving ingestion, aggregation, processing, curation, modeling, and analysis of high-volume data. Uses Cloudera, Databricks, AWS, Python, PySpark, SQL, and related tools to unify data assets, generate insights, support business intelligence, and enable fraud, security, and digital identity products. Partners with product, data science, platform, technology, client, and sales teams to deliver data solutions, prototypes, and enhancements.

Build and maintain large-scale backend data systems, batch and streaming pipelines, datamarts, and cloud data workloads. Develop AI-powered agents for anomaly detection and workflow automation, using Scala, Python, Java, Spark, Cloudera, Databricks, and AWS. Support platform reliability, data integrity, downstream analytics, documentation, Agile collaboration, and team mentoring.

Designs and deploys generative AI and machine learning solutions for AML transaction monitoring. Builds RAG, MCP, tool-calling, LLM fine-tuning, multimodal content generation, computer vision, evaluation, observability, and MLOps systems. Develops scalable cloud solutions using AWS, Azure, and Databricks, integrates models into production applications, and ensures performance, security, and compliance. Collaborates with technical and business stakeholders while applying current AI/ML research.

Build data engineering solutions that ingest, aggregate, unify, and analyze high-volume data from diverse sources. Develop data pipelines and models using Cloudera, Databricks, AWS, Hadoop, Python, and SQL. Partner with product, data science, platform, technology, client, and sales teams to deliver actionable insights, prototypes, business intelligence, and innovative cyber, fraud, security, and digital identity products.

Build and maintain scalable full-stack, data-intensive applications for Mastercard’s experimentation platform. Develop frontend, backend, APIs, and data-processing layers using Java, Spring Boot, TypeScript, React, Databricks, and Spark. Contribute to architecture, system design, testing, code reviews, performance optimization, and operational excellence. Collaborate with Product, Data Engineering, QA, and global development teams to deliver analytics-driven solutions, mentor colleagues, and improve engineering quality and scalability.

Lead design and build cloud-native batch and real-time data platforms. Develop scalable ETL/ELT and streaming pipelines (Spark, Kafka, Flink), ensure data quality, governance, observability, security, cost optimization, and drive platform engineering excellence and team mentorship.

Designs, automates, and optimizes enterprise Windows platforms using SCCM, Ansible, PowerShell, and Infrastructure as Code. Responsibilities include OS deployment, patching, software distribution, compliance enforcement, server build pipelines, CI/CD integration, self-service delivery, troubleshooting, capacity planning, and platform modernization. The role leads Wintel engineering workstreams, establishes automation standards, collaborates with infrastructure and security teams, and supports scalable, secure, and highly available services.

Modernize security operations by building SOAR playbooks, AI-augmented investigation workflows, prompt libraries, Jupyter notebooks, response tooling, and integrations across SIEM, EDR, identity, ticketing, threat intelligence, and AI platforms. Partner with SOC and DFIR teams to automate high-effort processes, support major incidents, optimize response outcomes, maintain reusable tooling, and contribute to product roadmaps and technical documentation.

Lead and grow agile engineering teams to design, build, and operate large-scale, distributed payment systems. Provide hands-on technical leadership across architecture, code quality, testing, CI/CD, security, and cloud-native deployments while partnering with product and program teams to ensure predictable, high-quality delivery.