Top AWS Jobs in Pune

Designs, builds, and supports scalable backend services, distributed data pipelines, REST APIs, and enterprise data ingestion frameworks for a multitenant B2B SaaS platform. The role owns features end to end, improves scalability and reliability, troubleshoots production issues, collaborates with product and architecture teams, conducts code reviews, mentors engineers, and applies modern cloud, data, CI/CD, and AI-assisted development practices.

Build responsive, scalable web applications with reusable UI components and modern front-end architectures. Integrate LLMs and generative AI capabilities, including chatbots, copilots, recommendation engines, intelligent search, RAG workflows, and AI agents. Collaborate with product, UX, data science, and backend teams; integrate APIs and microservices; participate in architecture decisions, code reviews, Agile delivery, and monitoring of AI-powered experiences.

Provides technical leadership for DevOps teams, defining infrastructure automation, CI/CD, cloud security, and Azure strategies. Designs and implements large-scale Azure solutions, manages Infrastructure as Code, improves software delivery processes, addresses infrastructure technical debt, and mentors engineers. The role also requires stakeholder communication, strategic planning, security expertise, and leadership of high-performing teams.

Lead QA strategy and governance for a large-scale payment gateway consolidation program migrating legacy gateways to a .NET platform. Manage and scale the testing team, define functional and non-functional testing, enforce quality gates, and validate data migration, payment routing, APIs, tokens, subscriptions, and transaction flows. Oversee performance, resilience, security, PCI compliance, enterprise integrations, and legacy systems across cloud and on-premises architectures.

Design, build, and support production B2B application features across React and TypeScript front ends and Java Spring Boot APIs. Modernize legacy Oracle ADF and JSF systems, collaborate with Product and Design, improve code quality and test coverage, participate in on-call and incident response, address security findings, mentor engineers, and guide technical decisions. The role also involves developing AI workflows and evaluating AI-generated code.

Develop and maintain scalable backend web applications using Python and Django. Design APIs, middleware, database schemas, and queries; troubleshoot performance issues; write maintainable code; and collaborate with product, design, frontend, and DevOps teams. The role also supports cloud deployment and may grow into full-stack development.

Build and ship full-stack features for a cloud-native cybersecurity SaaS platform. Own development end to end, including design, testing, code review, deployment, and monitoring. Use and evaluate AI coding agents such as Claude and ChatGPT, contribute to agentic workflows and automation, develop APIs and data models, and maintain cloud-native services using containers and serverless or event-driven architectures. Collaborate with security researchers to transform vulnerability intelligence into platform capabilities while applying secure-by-design and responsible AI practices.

Design, deploy, and configure end-to-end Dynatrace observability for AWS-hosted digital services, including dashboards, alerting, integrations, and data collectors. Troubleshoot distributed microservices, containers, databases, and downstream dependencies. Apply AWS, Docker, ECS, infrastructure-as-code, automation, and CI/CD expertise while collaborating with cross-functional teams and documenting technical solutions. Candidates must present prior hands-on Dynatrace delivery experience, including implementation decisions, challenges, pitfalls, and rollout methods.

Develop and maintain Astro’s consumer-facing website and My Astro app. Collaborate across teams to translate requirements into technical solutions, write maintainable code, document designs, review code, and build automated tests. Support databases, AWS cloud resources, CI/CD pipelines, QA processes, and AI initiatives such as a centralized knowledge base. The role is suited to fresh graduates or candidates with up to one year of experience who are eager to grow in frontend and backend development.

Provide advanced technical support for enterprise customers using Chainguard Images. Investigate and resolve complex issues involving Linux, Docker, Kubernetes, CI/CD, GitHub, Helm, Terraform, and cloud platforms. Manage customer communications and SLAs, document issues, create knowledge-base content, escalate bugs and feature requests, collaborate cross-functionally, and participate in an after-hours on-call rotation.

Build quantitative models, machine learning pipelines, data pipelines, and investment analytics supporting equity research. Partner with research analysts to convert valuation, financial statement, earnings, and sector methodologies into scalable models. Source and engineer financial data, develop predictive and risk models, explore investment signals, productionize models, and document methodologies. Communicate findings to technical and business stakeholders while applying statistical, machine learning, NLP, and quantitative finance techniques.

Design, develop, operate, and optimize large-scale software-defined networking solutions for NVIDIA AI Cloud environments. Responsibilities include building OVS/OVN control and data plane software, Kubernetes networking services, observability systems, CI/CD pipelines, GitOps integrations, and secure gRPC/REST services. The role owns production reliability, incident response, performance tuning, and upstream open-source contributions while collaborating with SRE, DevOps, and network engineering teams.

Investigate, diagnose, test, document, and resolve SaaS application bugs across production and change releases. Create test cases, perform regression and sanity testing, analyze logs and databases, support monitoring and alerting, coordinate with development and infrastructure teams, and manage defects through closure. The role includes service management, root-cause analysis, security testing support, knowledge documentation, AI tool adoption, and on-call production support.

Plans, develops, automates, and executes functional, non-functional, performance, regression, and security tests for web products and APIs deployed on AWS. Tests API gateways, error handling, integrations, and performance; documents test plans and results; identifies defects and corrective actions; and collaborates with developers, DevOps engineers, and stakeholders in an Agile environment to improve software quality, security, scalability, and reliability.

Lead QA automation strategy and build end-to-end test frameworks for web, API, and AI-driven features. Develop UI and API automation, performance/load tests, CI-integrated quality gates, and deterministic validation for non-deterministic ML outputs. Partner with product and engineering to define acceptance, shift-left quality, and validate AI/vision outputs and generated test code.

Provides advanced technical support for enterprise telecom customers using 4G/5G cloud solutions. Responsibilities include handling complex tickets, analyzing performance and logs, troubleshooting Radio, Core, networking, and Linux issues, coordinating with R&D and product teams, conducting preventive analysis, documenting issues, and supporting round-the-clock managed services.

Provides technical support for enterprise telecom customers using cloud solutions, 4G/5G call-flow analysis, Nokia Core and Radio platforms, Linux, and networking technologies. Responsibilities include handling tickets, troubleshooting critical network issues, collecting logs, analyzing performance, coordinating with R&D and product teams, conducting preventive analysis, documenting issues, and supporting managed services around the clock.

Develop optimized Scala and Spark solutions from business requirements, collaborate with product teams, and support end-to-end functionality across UAT and production. Responsibilities include code reviews, impact analysis, Agile development, AWS cloud adoption, Jenkins-based deployment automation, DevOps best practices, issue resolution, and maintaining application quality and stability.

Build and operate multi-cloud infrastructure across AWS, Azure, and GCP; manage Kubernetes platforms, CI/CD, GitOps delivery, observability, security, networking, and disaster recovery. Develop automation and self-service tools, improve reliability and incident response, and help productionize AI workloads. The role partners closely with engineering and AI teams while supporting production systems in a fast-moving startup environment.

Own AWS infrastructure, CI/CD, infrastructure-as-code, observability, security, cost optimization, containerization, backup and disaster recovery. Lead incident response, create operational documentation, and mentor engineers on monitoring and operational practices. The role requires architectural decision-making, reliable deployments, cloud cost management, and collaboration with distributed engineering teams working across U.S. time zones.

Leads QA strategy and quality initiatives for AI products, architecting scalable automation frameworks and developing testing methodologies for RAG, hallucination, drift, and bias. Owns the AI quality charter and roadmap, influences architecture and cross-functional teams, coaches QA staff, and drives reliability and risk reduction. Bonus responsibilities include CI/CD, cloud platforms, MLOps monitoring, and AI workload performance testing.

Leads performance and scalability testing for distributed, production-scale systems. Builds shared load-testing frameworks, workload models, production-like Kubernetes and cloud environments, and automated CI/CD regression checks. Conducts load, stress, soak, and spike testing; performs capacity planning and root-cause analysis across APIs, databases, queues, and frontends. Establishes performance targets, interprets observability data, influences engineering decisions, and mentors junior team members.

Designs enterprise-scale AI, Generative AI, and Agentic AI architectures and platforms. Leads implementation of LLM, RAG, conversational AI, knowledge assistant, and multi-agent solutions; defines AI strategy, governance, observability, scalability, security, and responsible AI practices. Collaborates with stakeholders, engineers, data scientists, and delivery teams to translate business needs into AI solutions. Provides architecture reviews, customer engagement, technical leadership, and mentorship to AI engineering teams.

Lead end-to-end delivery and governance of enterprise AI, Generative AI, Agentic AI, and machine learning initiatives. Manage planning, budgets, resources, risks, dependencies, stakeholders, customer engagements, and cross-functional teams including architects, data scientists, engineers, and MLOps specialists. Drive discovery workshops, roadmap definition, agile delivery, executive reporting, responsible AI practices, cloud AI implementations, and production deployment while ensuring quality and business outcomes.

Leads enterprise architecture for scalable, multi-tenant SaaS systems and cloud-native platforms. Owns AWS architecture, distributed data pipelines, cost optimization, security, governance, and architectural standards. Drives GenAI and machine learning adoption across the software development lifecycle, builds self-service capabilities, guides engineering teams, conducts prototypes, and leads architecture reviews. Requires deep hands-on cloud, distributed systems, coding, infrastructure automation, AI/ML, and strategic technology leadership experience.