Top AWS Jobs in Pune

Provide L2/L3 production support and develop enterprise Adobe Experience Manager applications. Troubleshoot incidents, analyze logs, perform root-cause investigations, resolve defects, and manage integrations. Build and maintain AEM components, templates, workflows, services, and reusable solutions using Java, Sling Models, OSGi, HTL, and JCR. Support deployments, CI/CD, monitoring, performance tuning, cloud adoption, documentation, code reviews, and on-call operations.

Build, maintain, and optimize enterprise data platforms using Databricks, Apache Spark, cloud technologies, and DevOps tools. Develop CI/CD pipelines, support cloud migration and automation, monitor platform health, resolve incidents, and ensure security, governance, reliability, and operational standards. Collaborate with engineers, architects, product teams, stakeholders, and global support teams while participating in rotational production support shifts.

Supports enterprise data platform engineering through Databricks and Spark workload management, cloud platform operations, CI/CD automation, monitoring, incident resolution, production support, and platform modernization. The role collaborates with engineering, architecture, product, and business teams while maintaining security, governance, reliability, documentation, and operational standards. Rotational shifts provide global support coverage.

Design, build, and operate production machine learning systems for Bloomreach’s search platform. Responsibilities include query-time ML, LLM and catalog data pipelines, model serving, online features, evaluation, A/B testing, observability, and low-latency optimization across merchants, languages, and domains. The role partners with Search Quality engineers to deploy ranking and relevance models reliably and cost-effectively at scale.

Design scalable cloud and distributed-system architectures for major sports, media, and entertainment platforms. Lead client discovery, translate business goals into technical solutions, guide engineering implementation, support pre-sales, and establish architecture standards. The role covers microservices, APIs, data pipelines, integrations, security, performance, observability, and high availability. It also evaluates GenAI and agentic technologies, mentors technical teams, and communicates solutions to executives and engineers.

Designs and maintains a centralized AI platform and agentic framework supporting secure deployment, management, and integration of AI agents. Responsibilities include building scalable agentic solutions, developing unified APIs, integrating agents into applications, optimizing cloud-native workloads, ensuring security and compliance, conducting capacity planning, mentoring engineers, and shaping the platform roadmap.

Leads QA engineering strategy, automation architecture, CI/CD adoption, quality metrics, defect reduction, and testing across multiple software portfolios. Manages distributed QA and automation teams, including hiring, coaching, performance management, and career development. Partners with product, engineering, and delivery stakeholders on Agile planning, release readiness, risk management, and continuous improvement. Oversees API and UI automation, cloud tooling, AI-based testing, regulated healthcare test data, and quality reporting.

Deliver Microsoft 365, Entra ID, Azure, Intune, Windows Server, and identity migration projects across multiple client tenants. Execute discovery, pilots, cutovers, rollback planning, post-migration support, cloud infrastructure deployment, endpoint management, security configuration, automation, documentation, and service-desk handoff. The role requires client communication, multi-client or MSP experience, PowerShell scripting, after-hours cutovers, and strong hands-on expertise across Microsoft cloud and hybrid environments.

Build, operate, and automate Infovista’s hybrid infrastructure across on-premises data centers and public clouds. Develop Infrastructure as Code, manage Kubernetes and CI/CD platforms, implement observability and SRE practices, automate lifecycle management and remediation, and support security, compliance, FinOps, asset discovery, and self-service provisioning. Collaborate with product engineering, security, IT operations, network teams, and third-party partners while modernizing legacy environments and promoting reusable automation.

Leads the design and delivery of enterprise data and analytics solutions, including data engineering, governance, metadata, cloud platforms, Snowflake, DevOps, Kubernetes, and AI-enabled capabilities. Translates requirements into scalable architectures, reviews code and designs, mentors engineers, manages technical risks, and partners with business and governance stakeholders. Contributes hands-on to implementation, platform observability, data quality, security, semantic solutions, knowledge graphs, and generative AI applications.

Design and deliver production AI solutions for enterprise customers, including agentic AI systems, LLM applications, RAG pipelines, ML services, APIs, and LLMOps infrastructure. Lead technical customer engagements, pre-sales initiatives, architecture decisions, delivery workstreams, and escalations. Collaborate with engineering and product teams, deploy solutions across cloud and on-premises environments, establish responsible AI controls, and mentor junior engineers while ensuring measurable business outcomes.

Designs, develops, and supports scalable full-stack applications using Node.js, React.js, Next.js, and AWS. Leads architecture, cloud integrations, serverless and event-driven systems, API and microservices development, testing, performance optimization, security, and production implementation. Provides technical leadership through architecture and code reviews, incident resolution, mentoring, stakeholder collaboration, and continuous improvement of engineering practices.

Develop and improve scalable, secure software solutions using Java and Spring technologies. Build and manage RESTful APIs within a microservices and event-driven architecture, collaborate with product and engineering teams, participate in code reviews, implement automated testing, and follow secure coding and enterprise design practices. The role also supports API-first strategy, cloud technologies, CI/CD, database development, performance optimization, and technical excellence across the organization.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Design and maintain automated tests across APIs, backend services, web and mobile interfaces, and blockchain components. Define quality strategies, assess risks, analyze test results and incidents, and build CI/CD-integrated testing pipelines. Collaborate with engineering, DevOps, and product teams to improve testability, reliability, performance, security, and observability. Mentor engineers and, at Staff level, set cross-team technical direction for quality engineering, testing standards, frameworks, and tooling.

Leads and performs development, configuration, testing, release support, and production support for NICE Actimize AML platforms. Responsibilities include scenario and rule tuning, alert and case workflow management, troubleshooting in Linux and Oracle environments, migration support, SDLC compliance, stakeholder coordination, and team leadership. The role requires strong AML transaction-monitoring expertise, hands-on Actimize experience, and knowledge of banking systems, databases, scripting, and regulatory controls.

Lead the technical direction of Twilio Segment’s high-scale data platform and pipelines. Design fault-tolerant, cloud-native distributed systems, guide complex architectural decisions, optimize performance, cost, and reliability, and influence engineers and executives. Prototype and deploy AI and LLM solutions for data-processing challenges while mentoring teams and driving technical alignment across geographically distributed engineering organizations.

Monitor cloud, server, network, storage, and database environments; take corrective action to maintain uptime; create monitoring alerts and dashboards; develop infrastructure monitoring and reporting tools; optimize configurations; document operations; manage outages and escalations; and coordinate with engineering teams on high-priority tickets. The role supports evening or night shifts and values AI-enabled workflow automation.

Design, build, and lead scalable backend services and AI platform systems using Node.js, TypeScript, AWS, databases, and modern LLMs. Own architecture, reliability, security, observability, CI/CD, and performance across multiple teams. Mentor engineers, guide technical direction, collaborate cross-functionally, and ship agentic AI products in a startup environment. The role remains hands-on, covering distributed systems, APIs, cloud infrastructure, and end-to-end solution design.

Lead the development, training, deployment, and maintenance of machine learning and generative AI systems. Build predictive, NLP, chatbot, LLM, and agentic AI solutions using cloud platforms, big data technologies, databases, and ML frameworks. Design ETL pipelines, optimize data processing, and create analytics visualizations. The role also involves model fine-tuning, scalable AI application development, and collaborative problem-solving.

Build and maintain customer-facing dashboard features across frontend and backend using React.js, Spring Boot, and Python. Develop REST APIs, data pipelines, and responsive UIs; participate in design, code reviews, testing, and production support to improve reliability and performance. Collaborate with Product and Design to deliver features and continuously improve developer productivity and application quality.

Design, operate, and optimize cloud infrastructure and internal developer platforms across AWS, GCP, and Azure. Manage Kubernetes clusters, container systems, CI/CD pipelines, observability, infrastructure as code, security controls, and operational automation. Troubleshoot distributed systems, improve reliability and cost efficiency, and develop tooling with Go, Python, Terraform, and related technologies. The role also applies AI and machine learning tools to automate DevOps workflows and enhance developer productivity.

Design and implement quality engineering strategies for AI-powered applications, LLMs, RAG pipelines, copilots, and autonomous agents. Build scalable Python automation frameworks, AI evaluation pipelines, synthetic datasets, and validation workflows. Test APIs, microservices, distributed systems, and agent orchestration while integrating quality gates into CI/CD. Validate vector databases, model behavior, safety, observability, reliability, performance, and cloud deployments across Docker, Kubernetes, and major cloud platforms.

The posting provides no specific responsibilities, qualifications, compensation, location, travel, or clearance details. The title indicates a Generative AI engineering role focused on AWS technologies.

Designs and maintains scalable AWS, Snowflake, and dbt data pipelines and transformation frameworks. Builds ETL/ELT workflows, dimensional models, data quality frameworks, and optimized SQL workloads for structured and semi-structured data. Responsibilities include pipeline orchestration, monitoring, troubleshooting, CI/CD, legacy ETL modernization, documentation, production support, and collaboration with technical teams. The role also contributes to architecture discussions, Agile delivery, code reviews, and mentoring junior engineers.