Top AWS Jobs in Pune

Lead a 10–15-person engineering team building agentic AI systems from the ground up. Set architecture strategy for planning, memory, tool use, multi-agent coordination, guardrails, observability, evaluations, reliability, and safety. Drive adoption of an AI-Driven Development Lifecycle, partner with product managers on ambiguous requirements, oversee delivery and technical reviews, and support hiring, mentorship, career growth, and leadership reporting.

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.

Develop Java and Spring Boot software, REST/SOAP APIs, and cloud-based applications for fraud detection and identity proofing. Work with AWS, databases, Docker, Kubernetes, CI/CD tools, messaging frameworks, and event-based systems. Support Customer Protection Technology initiatives that help verify user identities and prevent fraud.

Develop and optimize Oracle SQL and PL/SQL solutions, including data modeling, performance tuning, OLTP, data warehousing, and ETL/ELT processes. Use Informatica, test automation, CI/CD pipelines, AWS-based Oracle environments, and job scheduling tools such as Autosys or Airflow. Apply strong quality practices while troubleshooting issues and communicating effectively.

Develop software and data warehousing solutions supporting fraud detection and identity proofing. The role involves Java, Python, SQL, Snowflake, Yugabyte, ETL/ELT, cloud deployment, containerization, continuous integration, automated deployments, event-based systems, and structured and unstructured data stores. Responsibilities support AWS-based customer protection technology and may involve Docker, Kubernetes, Jenkins, Kafka, Terraform, and related data platforms.

Develops and supports cloud-based data solutions using AWS services, Python, PL/SQL, ETL, PostgreSQL, and PySpark. Responsibilities include data migration and ingestion, analytical problem-solving, debugging, API integration, and potentially Power BI support. The role requires strong cloud engineering experience and familiarity with AWS S3, Glue, Lambda, and IAM roles.

Build and productionize customer-facing data and AI solutions on Databricks. Own architecture, design, implementation, deployment, and technical delivery of scalable systems spanning data engineering, machine learning, AI models, pipelines, and applications. Guide enterprise customers, collaborate with engineering and product teams, resolve implementation issues, document solutions, and create reusable accelerators. The role requires customer immersion, stakeholder management, technical project delivery, and approximately 20% travel.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Principal Full Stack Engineer responsible for setting technical direction while remaining deeply hands-on across Laravel, PHP, Next.js, AWS, and GCP. The role owns platform architecture, leads cross-team technical initiatives, improves testing, CI/CD, observability, and scalability, and mentors engineers through influence. Responsibilities also include evaluating AI technologies, supporting event-driven and modular architectures, guiding roadmap trade-offs, and solving complex system-level challenges across product squads.

Design and deliver scalable full-stack SaaS features using Laravel, PHP, React or Next.js, and AWS. Build APIs, integrations, reusable components, tests, and CI/CD improvements while contributing to architecture, performance, accessibility, localization, and compliance. Collaborate with product, design, and QA; provide mentorship; support event-driven architecture and modular monolith evolution; and lead test coverage, documentation, and engineering standards.

Develop and improve scalable, secure software solutions using Java, Spring Boot, microservices, APIs, asynchronous messaging, and distributed systems. Responsibilities include database integration, performance optimization, observability, resilience, unit testing, secure coding, code reviews, and cross-functional collaboration. The role also involves evolving infrastructure and deployment pipelines, guiding colleagues, managing risk and controls, and contributing to technology strategy. Preferred experience includes AWS, containerization, AI-assisted development, and payments processing.

Design, build, and maintain cloud infrastructure and automation using IaC (Terraform) across Azure and AWS. Develop and maintain CI/CD pipelines (ADO, Jenkins, GitHub Actions, GitLab CI/CD), integrate security and testing into delivery, use AI-assisted tools to improve productivity, and support operations, monitoring, and incident resolution.

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.

Designs and executes UI performance, API, and end-to-end automated tests using Playwright or Selenium, Artillery, JavaScript or Java, SQL, and CI/CD pipelines. Analyzes test results, debugs failures, performs root cause analysis, validates data, and collaborates with development and product teams to improve quality. The role also tests AI-driven features and uses AI coding tools. Preferred experience includes cloud technologies, Datadog, Jenkins, Allure, Docker, and permission testing.

The Engineering Specialist is responsible for frontend application development and backend integration, focusing on building scalable web applications, collaborating with teams, and adhering to architecture standards.

Designs, develops, and deploys autonomous and multi-agent AI systems for business workflows. Builds agentic applications using LLMs, RAG, vector databases, tool calling, memory, and orchestration frameworks. Integrates agents with enterprise applications, APIs, databases, CRMs, and ERPs while developing backend services and APIs. Optimizes solutions for scalability, security, performance, and cost, and collaborates with product, engineering, and business teams to identify automation opportunities.

Monitor, investigate, and respond to cybersecurity events across a global enterprise. Responsibilities include SIEM monitoring, threat hunting, incident response, endpoint and email threat analysis, DLP investigations, firewall policy review, vulnerability assessments, SOC documentation, and playbook development. The role also mentors junior analysts and serves as a senior escalation point during shift operations.

Supports and maintains privileged access management infrastructure using CyberArk, BeyondTrust, and HashiCorp Vault. Responsibilities include platform monitoring, upgrades, integrations, automation, credential rotation, access governance, troubleshooting, incident resolution, documentation, and audit compliance. The role integrates PAM solutions with enterprise applications, Kubernetes, cloud platforms, and CI/CD pipelines while partnering with global security, infrastructure, DevOps, and application teams.

Leads the Infrastructure Platform Engineering team within a global SRE organization. Responsibilities include people management, hiring, prioritization, operational excellence, incident response, reliability improvements, and cross-functional coordination. The role requires technical depth in Kubernetes, containerized platforms, public cloud, observability, infrastructure as code, CI/CD, and automation, along with experience managing distributed engineering teams supporting business-critical production systems.

Lead the design, development, deployment, and optimization of production-grade Generative AI systems. Build RAG pipelines, fine-tune foundation models, develop AI agents, establish evaluation and observability frameworks, and integrate multimodal AI capabilities. Collaborate with product, engineering, data, and business stakeholders to deliver scalable, reliable, secure, and responsible AI solutions.

Senior technical leader responsible for engineering strategy, platform-scale architecture, and delivery across multiple teams. Owns technical roadmap for shared services, champions event-driven architectures (Kafka), provides hands-on guidance for Java microservices, ensures reliability, observability, and security, and partners with senior business and technology stakeholders to drive execution and scalability.

Lead delivery of cloud data platform solutions: design and build pipelines on Snowflake/Databricks using SQL, PySpark and ETL tools, implement CI/CD, work client-facing to run workshops, mentor junior staff, and identify business value.

Lead engineering teams to deliver high-quality software by providing technical guidance, mentoring, overseeing delivery timelines, improving processes and tooling, enforcing standards, and collaborating with product and stakeholders. Drive DevOps/CICD, cloud, containerization, IaC, code quality and automation practices within the Mortgages digital domain.

Own and operate secure, highly available AWS and GCP infrastructure, MongoDB Atlas, infrastructure-as-code, Kubernetes, CI/CD, observability, and incident response. Lead a three-person DevOps team, self-host open-source tools, improve security and privacy practices, define reliability standards, automate operations, and collaborate across engineering, product, and security teams.

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.