Top AWS Jobs in Pune

Own the full sales cycle across ASEAN, from prospecting through enterprise close. Conduct technical discovery, connect customer needs to business outcomes, and collaborate with Sales Engineering, Product, AWS, and regional partners on demos and proofs of concept. Manage territory strategy, pipeline, forecasting, account planning, procurement, security, and data residency discussions. Sell to founders, CTOs, engineering leaders, and platform teams, then transition customers to Customer Success for onboarding and expansion.

Serve as a Cloud Economics partner for APAC engineering teams by analyzing AWS, GCP, Azure, and AI/LLM costs. Responsibilities include anomaly detection, root-cause analysis, team-level forecasting, showback reporting, cost modeling, governance, and engineering consultation. The role requires advanced analytical SQL, cloud FinOps expertise, LLM cost understanding, written technical synthesis, and collaboration through Jira, Confluence, Slack, and cloud cost platforms.

Lead enterprise customer onboardings for Neo4j AuraDB and self-managed graph clusters across hybrid and multicloud environments. Architect secure networking, identity, infrastructure, data ingestion, observability, and application integrations. Serve as the senior escalation point and mentor for a four-person onboarding team while developing reusable technical blueprints, automation, architecture documentation, and operational runbooks.

Design and develop responsive web applications and reusable UI components while building AI-powered experiences. Integrate LLM and AI services, develop chatbots and copilots, implement prompt engineering and RAG architectures, and optimize applications for scalability, security, performance, and usability. Collaborate with product, design, data science, and backend teams, contributing to testing, deployment, code reviews, and production support.

Design, develop, and maintain high-performance Go backend applications, scalable microservices, RESTful and gRPC APIs, and distributed systems. Integrate databases, messaging platforms, cloud services, and third-party systems while implementing monitoring, logging, observability, CI/CD, and DevOps practices. Participate in code reviews, optimize reliability and performance, troubleshoot production issues, and collaborate with cross-functional teams to deliver quality solutions.

Prioritize and quantify cyber risk by correlating vulnerability data, threat intelligence, attack paths, adversary behavior, and business impact. Model exposure scenarios across identity, cloud, network, and endpoint environments; partner with red and blue teams; translate technical findings into business risk; track remediation and residual risk; communicate risk posture to security leadership, GRC, and engineering stakeholders; and influence security investments based on measurable risk reduction.

Designs, builds, and deploys production AI systems, including LLM pipelines, RAG applications, agentic workflows, data platforms, APIs, and user-facing interfaces. Responsibilities include evaluating models, creating guardrails and observability, processing large-scale behavioral data, deploying cloud-based services, troubleshooting production systems, and iterating based on user behavior. The role requires full-stack development, strong Python and ML fundamentals, product thinking, and effective remote collaboration.

Build and maintain CI/CD pipelines, cloud infrastructure, and automated deployment processes. Implement Infrastructure as Code, manage Docker and Kubernetes environments, monitor system reliability, troubleshoot production issues, and support scalable software delivery. Collaborate with development and operations teams in an Agile environment while applying security and operational best practices.

Designs, develops, supports, monitors, and optimizes Apigee APIs and enterprise integrations connecting Connected CE applications with Oracle EBS, ServiceNow, Salesforce, and other platforms. Responsibilities include API proxy and security configuration, SQL development, production monitoring, troubleshooting, root cause analysis, release management, documentation, and ITIL incident, problem, and change management. The role collaborates with development, QA, infrastructure, and business teams.

Leads data engineering, analytics, predictive modeling, and Generative AI initiatives. Designs scalable Microsoft Fabric infrastructure, ETL/ELT pipelines, governance frameworks, and data models; develops and deploys predictive models; analyzes complex datasets; automates financial reporting and reconciliation; and partners with business, finance, and analytics teams. Provides technical leadership, mentors junior engineers, establishes reporting standards, and promotes data literacy.

Designs and maintains large-scale automated testing frameworks, libraries, infrastructure, and test suites for enterprise SaaS applications, APIs, services, and distributed systems. Develops production-quality automation, integrates tests into CI/CD pipelines, troubleshoots application and production issues, and expands test coverage using AI-assisted development tools. Collaborates with engineering, DevOps, product, and QA teams to improve reliability, observability, maintainability, and automation efficiency.

Extracts, transforms, and analyzes data to identify insights, conduct hypothesis testing, and support sales and client research initiatives. Manages custom analytics projects, presents findings, validates results, improves processes through automation, develops products and reporting tools, and mentors analysts. Collaborates across teams and supports media measurement offerings spanning digital, mobile, OTT, and television.

Design and build production-grade data pipelines, services, APIs, and data models powering analytics and AI features. Optimize performance and scalability, implement data validation, monitoring, security, and multi-tenancy, and lead troubleshooting. Establish technical approaches, participate in reviews and CI/CD, collaborate across product and data teams, and mentor engineers.

Develop and launch modules, APIs, and product features for Onit’s legal technology platform. Work across backend and frontend development using Ruby on Rails, React, JavaScript, and PostgreSQL; collaborate with designers and project managers; estimate tasks; automate testing; and contribute within an Agile, AWS-based development environment. The role emphasizes backend expertise with growth toward full-stack development and mentoring junior developers and QA resources.

Build and maintain data pipelines, backend services, and APIs supporting analytics and AI features. Develop SQL queries, data models, validation checks, automated tests, and production-grade code. Troubleshoot data and application issues, participate in Agile ceremonies and code reviews, document solutions, and collaborate with senior engineers, architects, product managers, data scientists, and QA.

Build and operate an AI-first developer platform, including CI/CD pipelines, Kubernetes-based tooling, developer products, and autonomous agents for remediation, upgrades, scanner triage, and credential rotation. Establish secure agent controls, observability, infrastructure as code, and zero-downtime practices. Run measurable experiments, improve DORA performance, collaborate with delivery teams, and influence the platform roadmap in a regulated environment.

Develop and validate AI and machine learning models across credit risk, marketing, fraud, and account management domains. Use GCP to automate workflows, prepare training data, develop algorithms, and execute AI validation projects. Collaborate with stakeholders across geographies, support model risk management policies, ensure regulatory compliance, and apply industry best practices. The role requires advanced Python and SQL skills, model validation experience, and knowledge of financial risk models.

Leads Model Risk Management validation for statistical, machine learning, generative AI, and agentic AI models. Manages and mentors model validators, oversees complex validation projects, performs hands-on technical reviews, develops validation methodologies, assesses documentation and monitoring, advises model developers and business owners, and strengthens governance to meet regulatory requirements. The role also handles auditor and regulator inquiries and requires expertise in quantitative modeling, big data, cloud environments, and programming.

Own the architecture of a multi-tenant, data-intensive analytics capability within an enterprise SaaS platform. Select the analytical data platform, design canonical data models, define API contracts, establish performance and availability requirements, and enforce tenant isolation. The role includes hands-on query and performance engineering, architecture decision records, design and code reviews, and translating technical constraints across Product, Design, and Engineering.

Leads engineering teams and provides hands-on technical leadership for scalable Java software solutions. Responsibilities include architecture, system design, code reviews, mentoring, delivery oversight, engineering governance, risk management, testing, cloud-native development, and stakeholder alignment. The role shapes technical strategy, improves engineering processes, manages technical debt, and ensures secure, reliable, high-quality software delivery.

Designs and maintains secure CI/CD pipelines, cloud and on-premises infrastructure, infrastructure-as-code, container platforms, monitoring, and observability solutions. Embeds security controls across the software lifecycle, manages vulnerability scanning and remediation, supports incident response, ensures compliance, and improves deployment reliability. Partners with development, QA, application support, architecture, and security teams while maintaining documentation and operational runbooks.

Leads the strategy, roadmap, engineering direction, adoption, reliability, security, and service performance of enterprise cloud data and database platforms. Owns Snowflake, Databricks, Microsoft Fabric, relational databases, NoSQL services, integration foundations, automation, and self-service capabilities. Partners with engineering, architecture, operations, security, risk, vendors, and internal consumers to modernize platforms, improve reliability and adoption, manage costs, meet regulatory obligations, and deliver measurable business value. Leads globally distributed technical teams and influences senior stakeholders and strategic vendors.

Lead and scale distributed engineering teams, drive technical best practices, manage project timelines and risks, oversee hiring and career development, and ensure scalable, secure, maintainable architectures while collaborating with product and stakeholders.

Lead the strategy, development, and operation of secure, scalable software and cloud platforms. Manage engineering teams, guide architecture and technical governance, and oversee AWS, OpenShift, and Kubernetes environments. Drive engineering standards, observability, resiliency, risk management, incident escalation, and AI-enabled development practices. Partner with product, operations, risk, compliance, and senior leadership while supporting workforce planning, talent development, and platform investment decisions.

Build and manage cloud platforms, frameworks, and CI/CD infrastructure across production environments. Lead DevOps engineering practices, secure SDLC implementation, cloud architecture, infrastructure as code, Kubernetes and containerization initiatives. Mentor engineers, establish technical design standards, and promote DevOps culture. Design AI-assisted operational automation with appropriate security controls, governance, auditability, and human oversight for incident response, infrastructure management, deployments, and developer productivity.