Top AWS Jobs in Pune

Designs and implements production-ready data solutions, including pipelines, orchestration, ingestion, distributed computing, and cloud services. Responsibilities include gathering technical requirements, writing tested code, conducting design and code reviews, troubleshooting defects, participating in Agile ceremonies, and evaluating emerging technologies. The role collaborates with architects, client-facing teams, and project stakeholders while applying data management, DevOps, security, ETL, and data warehousing practices.

Design and develop scalable, secure software systems, including high-performance microservices, distributed systems, REST/gRPC APIs, messaging, search, and cloud storage solutions. Collaborate with front-end engineers, monitor and optimize services, troubleshoot production issues, mentor junior engineers, and drive end-to-end technical execution using DevOps practices.

Sell Dynatrace to enterprise customers through a land-and-expand approach. Own a 40-account territory (0–2 customers, 35–40 prospects), drive new-logo acquisition, engage VP/C-level stakeholders, run product demos and market initiatives, coordinate with sales engineering, marketing, legal and finance, and ensure successful implementations and upsell opportunities.

Leads end-to-end development of real-time payment network products using Java, Spring Boot, microservices, event-driven architecture, messaging, CI/CD, and cloud-native platforms. Designs scalable solutions, ensures quality, performance, and security benchmarks, guides development teams, conducts proofs of concept, selects technologies and patterns, improves automation and defect metrics, and coordinates with product, architecture, business, testing, vendor, and global stakeholder teams in an Agile SAFe environment.

Manage AWS infrastructure across Prod/Stage/Dev, handle incident management and root cause analysis for core AWS services, ensure availability and performance, monitor health and cost, perform access management and resource optimization, support Terraform-based provisioning, automate operational tasks, and work with CI/CD pipelines while collaborating with Engineering, Security, and Data teams.

Lead the technical direction, reliability, security, and scalability of platform infrastructure, deployment pipelines, and operational automation, especially within a FedRAMP environment. Design infrastructure-as-code, improve observability and incident response, build microservice automation, guide platform migrations, partner with security and compliance teams, and mentor engineers.

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.

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.

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.

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.

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.

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.

Designs and reviews security architecture, performs threat modeling and cloud assessments, responds to incidents, manages security tools, and advises engineering teams on secure development. Conducts code and vulnerability reviews using OWASP and MITRE ATLAS, evaluates AI and LLM security risks, and supports security monitoring, risk assessment, data protection, and compliance across applications, infrastructure, and endpoints.

Leads SailPoint’s Data and AI Platform Engineering organization, setting technical strategy and managing engineering teams responsible for large-scale data processing, machine learning infrastructure, streaming, data governance, search, and distributed systems. Oversees platform roadmaps, architecture, delivery, reliability, stakeholder collaboration, talent development, and thought leadership across technologies including Flink, Spark, Snowflake, Iceberg, Airflow, and OpenSearch.

Architect, develop, and scale secure cloud-native software, microservices, REST/gRPC APIs, and distributed systems for security operations and exposure management. Lead end-to-end technical execution, mentor engineers, collaborate on high-performance interfaces, and monitor and optimize system health. The role requires expertise in Java, Spring, containers, cloud platforms, observability, software development lifecycle practices, and highly available infrastructure.

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.

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.