Top AWS Jobs in Pune

Designs and governs enterprise SAP BTP architectures, including global account structures, side-by-side extensions, CAP/RAP applications, identity and access frameworks, API governance, runtime selection, and automated DevOps delivery. The role manages Cloud Foundry, Kyma, and serverless deployment strategies, platform security, cost optimization, telemetry, and integration with S/4HANA. It requires 6–10 years of architecture or cloud engineering experience, including at least three years focused on SAP BTP, plus a mandatory SAP certification.

Develop and maintain complex Workday integrations using Workday Studio, EIBs, Core Connectors, APIs, SOAP, REST, and RaaS. Build secure data pipelines, custom transformations, encryption and SFTP delivery workflows. Troubleshoot performance issues, web service limits, and high-volume processing. Lead regression testing for Workday releases and support integrations with payroll, benefits, and third-party systems.

Designs and governs secure multi-cloud architectures across AWS, Azure, and GCP. Responsibilities include zero-trust landing zones, IAM and RBAC, encryption and key management, container and Kubernetes security, CSPM and CWPP integration, policy-as-code automation, DevSecOps security testing, and incident-response readiness. The role requires extensive enterprise infrastructure security experience, cloud security architecture expertise, infrastructure-as-code knowledge, threat modeling skills, and a mandatory security certification.

Designs and operates cloud-native infrastructure, automated CI/CD pipelines, infrastructure-as-code, Kubernetes platforms, observability systems, and secure configuration management. The role focuses on high availability, disaster recovery, cluster scaling, fault tolerance, incident response, and root-cause analysis. Candidates need substantial DevOps or systems engineering experience, advanced Kubernetes and Terraform expertise, Linux and scripting knowledge, and a mandatory AWS, Azure, or Kubernetes certification.

Manage the reliability, performance, scalability, and security of high-volume cloud databases. Responsibilities include designing highly available clusters, optimizing queries, managing replication and disaster recovery, building monitoring dashboards and alerts, automating maintenance with Python, Go, or Bash, resolving production bottlenecks, and enforcing data protection controls. The role requires extensive database administration or reliability engineering experience, cloud-native database expertise, infrastructure automation skills, and a relevant professional certification.

Designs and governs secure, scalable multi-cloud and hybrid-cloud infrastructure across AWS, Azure, and GCP. Responsibilities include enterprise network architecture, automated Terraform/OpenTofu landing zones, high availability and disaster recovery, identity and tenancy governance, cloud migration strategy, and architecture review boards. The role requires leading complex cloud platforms, enforcing organizational policies, and supporting regulated environments, with expertise in Kubernetes, serverless architectures, and cloud-native networking.

Lead NLP and LLM fine-tuning initiatives using PEFT methods, curate and validate training datasets, implement RLHF and DPO alignment, optimize models through quantization, design evaluation benchmarks, manage distributed multi-GPU training, and prepare model checkpoints for cloud deployment. Requires advanced expertise in Python, PyTorch, transformer architectures, CUDA, and NLP, plus a mandatory AWS or Google Cloud machine learning certification.

Design, develop, and stabilize production-grade multi-agent AI systems. Build orchestration architectures, state machines, secure tool execution, memory layers, human approval workflows, reasoning patterns, and observability frameworks. Integrate enterprise APIs, databases, file-processing environments, and vector storage while mitigating hallucinations, infinite loops, and token-budget issues. The role requires strong Python or TypeScript, asynchronous programming, distributed systems, and dedicated production experience with autonomous agents.

Lead FinOps governance and cloud cost optimization across AWS, Azure, and GCP. Responsibilities include analyzing billing telemetry, identifying waste, managing Reserved Instances and Savings Plans, building forecasting models and policy-as-code alerts, developing cost visibility dashboards, coordinating remediation with DevOps and SRE teams, and supporting vendor pricing negotiations. The role requires extensive cloud financial management experience and a FinOps certification.

Designs and optimizes production-grade Retrieval-Augmented Generation systems for enterprise AI applications. Responsibilities include architecting retrieval pipelines, optimizing context windows and token usage, implementing re-ranking and semantic caching, building document chunking and ingestion workflows, governing hybrid vector search, and measuring retrieval accuracy, hallucination rates, latency, and cost. The role requires deep expertise in Python, vector databases, embeddings, LLM orchestration, SQL, and cloud data engineering, plus a mandatory professional cloud or analytics certification.

Designs, deploys, and optimizes production vector databases for GenAI and semantic search applications. Builds embedding ingestion pipelines, configures ANN and hybrid search indexes, implements metadata filtering, monitors cluster performance and costs, and supports scalable retrieval infrastructure. Collaborates with AI and data engineering teams on embedding models and runtime requirements.

Leads the design and delivery of scalable enterprise data platforms supporting financial research, analytics, automation, and AI solutions. Responsibilities include architecting data lakes, warehouses, and lakehouses; defining ingestion, transformation, governance, and observability frameworks; managing multiple large-scale programs; mentoring engineering teams; and ensuring scalability, security, performance, and operational resilience.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Lead development and deployment of production ML models and analytics for wealth management and retail banking. Analyze large datasets, design predictive solutions (segmentation, churn, personalization, risk), build data pipelines and feature engineering, monitor model performance, drive MLOps and responsible AI practices, and communicate insights to stakeholders.

Design user experiences and interfaces for web and mobile products using human-centered design principles. Conduct user research, create prototypes, iterate designs, communicate recommendations to clients, and contribute to design systems. Collaborate with product managers, designers, and Agile teams to gather requirements and deliver development-ready designs. Apply expertise in typography, color, spacing, interaction design, and design tools while incorporating stakeholder feedback.

Build and own full-stack products end to end, from requirements gathering with operational teams through design, development, deployment, instrumentation, and iteration. Develop GenAI capabilities using LLMs, agents, RAG, and workflow automation. Manage reliability through testing, code review, production support, and technical debt reduction. Collaborate clearly with nontechnical stakeholders, and at senior levels lead ambiguous projects and mentor engineers.

Develop audience segments, customer profiles, and predictive models using behavioral, transactional, viewing, and digital data. Apply statistical and machine learning methods for clustering, churn, propensity, content affinity, and customer value analysis. Conduct exploratory analysis, evaluate models, support campaign measurement, and translate findings into business recommendations. Collaborate with Marketing, Product, Media Sales, and Content teams while presenting insights to technical and non-technical stakeholders.

Support technology audits covering IT general controls, application and infrastructure controls, cloud security, cybersecurity, access management, and change management. Test controls, review cloud configurations and CI/CD practices, automate audit procedures with Python or SQL, analyze logs and datasets, prepare audit workpapers and reports, and collaborate with engineering, security, compliance, and audit stakeholders on risk remediation.

Designs, scales, and maintains highly available cloud infrastructure for production SaaS applications. Responsibilities include improving observability and alerting, leading incident response and postmortems, modernizing CI/CD pipelines, managing infrastructure as code, supporting data layers, partnering with engineering teams, and enforcing security and compliance standards across cloud environments.

Build and maintain complete web applications and backend services from architecture through production. Develop responsive interfaces, APIs, background workers, data pipelines, and AI integrations. Manage cloud deployment, monitoring, CI/CD, load testing, and system optimization. The role requires end-to-end ownership, strong full-stack development skills, relational database expertise, event-driven architecture knowledge, and the ability to work independently while communicating technical tradeoffs.

Manage and maintain AWS database services, including provisioning Aurora, Redis, and MongoDB with Terraform. Optimize performance, troubleshoot incidents, execute patching and upgrades, and maintain infrastructure automation and CI/CD pipelines. Create operational documentation and collaborate with application teams on schema design, query optimization, capacity planning, availability, and security.

Develop and maintain Python applications, APIs, workers, integrations, and AWS Lambda functions. Manage AWS and Linux environments, infrastructure automation, deployments, CI/CD pipelines, and production support. Improve reliability through monitoring, canary releases, rollback, and recovery practices. Maintain databases, messaging, authentication, security, dependencies, and external integrations. Apply AI-assisted development tools with human review and security validation. The role requires ownership of software delivery from application code through production.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics teams.

Owner of SailPoint's SaaS QMS: maintain policies, coordinate evidence and audits across multiple frameworks, run internal audit and CAPA programs, embed quality in engineering workflows, support GxP vendor qualification for life-sciences accounts, and drive evidence automation and continuous improvement.

Supports and improves enterprise infrastructure across managed services environments. Administers Zabbix monitoring, Linux and Windows servers, Active Directory, virtualization, containers, Microsoft 365, databases, networking, and endpoint platforms. Provides Tier 2/3 support, incident and root-cause analysis, automation, documentation, and continuous improvement while supporting multiple customer environments.