Top AWS Jobs in Pune

Build and maintain reliable, scalable Java-based microservices for SailPoint’s cloud identity analytics SaaS product. Responsibilities include technical design, feature implementation, code reviews, testing, production performance instrumentation, CI/CD improvements, and collaboration across an agile engineering team. The role will eventually own functional areas, contribute to architecture decisions, and lead scalable microservice enhancements.

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.

Develop and maintain scalable, highly available enterprise applications and portals using Java/Spring Boot. Design cloud-native microservices, implement REST APIs, ensure operational excellence through monitoring, automation, incident management, capacity planning, and collaborate with global cross-functional teams to meet security, compliance, and performance goals.

Lead architecture, design, coding, testing, and delivery of a cloud-native experimentation and analytics platform. Build scalable services, APIs, data pipelines, and analytical capabilities using AWS, Azure, Databricks, Spark, and PySpark. Drive modernization, system design, technical strategy, architecture reviews, security, reliability, observability, and continuous delivery. Coordinate globally distributed teams, collaborate cross-functionally, mentor engineers, and ensure high-quality delivery of data-intensive software solutions.

Design, build, and operate Dev Cloud and internal developer platform capabilities. Own Docker Desktop developer workflows, extend Backstage (templates, plugins, catalogs), build cloud-native services on AWS, automate IaC and CI/CD, drive observability and security, onboard application teams, troubleshoot developer issues, and mentor engineers to improve developer experience and platform reliability.

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.

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.

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.

Own the strategy, roadmap, requirements, and delivery of AI-powered financial reconciliation products. Lead discovery with financial stakeholders, translate reconciliation and regulatory needs into BRDs, epics, and user stories, prioritize backlogs, and guide engineering and AI/ML teams from concept through launch. Define performance KPIs, support pricing and go-to-market activities, and ensure compliance, governance, auditability, and financial controls across enterprise reconciliation workflows.

Supports the design, requirements, testing, documentation, and delivery of AI-powered and API-enabled financial reconciliation products. Maintains backlogs, coordinates agile execution, analyzes API telemetry and product metrics, supports AI feature evaluation, and documents controls for agentic workflows. Collaborates with engineering, operations, finance, compliance, and business stakeholders on discovery, integrations, client onboarding, go-to-market materials, roadmap planning, and leadership reviews.

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 the architecture, development, scaling, and modernization of Java-based AWS microservices and full-stack web interfaces. Build RESTful APIs, responsive UIs, automated testing pipelines, telemetry, and continuous delivery workflows. Optimize distributed systems for resilience, scale, and low latency while driving architecture discussions, code reviews, technical sizing, and mentorship across the engineering organization.

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.