Top AWS Jobs in Pune

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.

Develop and scale generative AI applications, including RAG pipelines, answering engines, extraction tools, and content-authoring solutions. Build and optimize prompts, embeddings, vector databases, caching layers, and other GenAI components. Collaborate with data scientists and clients to deploy machine learning pipelines and manage the ML lifecycle using cloud platforms such as AWS, Azure, and GCP. Apply software engineering, DevOps, CI/CD, and distributed computing practices to create scalable, user-focused AI products.

Collaborates with internal and client teams to analyze business problems, translate requirements into technical designs, and implement technology solutions. Develops technical features using appropriate methodologies, testing practices, and technology stacks. Supports project implementation planning, interprets business requirements, and contributes to small- and medium-scale client engagements involving databases, programming, cloud platforms, and analytics.

Leads the architecture, design, and delivery of secure, scalable, resilient infrastructure across data center, hybrid cloud, and distributed environments. Partners with engineering, security, networking, cloud, product, and program teams to deliver modernization initiatives, automation, Infrastructure-as-Code, platform standardization, and operational excellence. Develops architecture standards and roadmaps while ensuring availability, compliance, disaster recovery, performance, and security for mission-critical platforms.

Operate and improve multi‑cloud (AWS/Azure/hybrid) platforms with SRE practices: lead incident triage and RCA, manage Kubernetes and CI/CD pipelines, implement IaC and automation, enhance observability/AIOps, and drive DevSecOps and operational excellence across global 24x7 operations.

Lead the machine learning engineering team in building enterprise AI solutions, including multi-agent systems, multimodal pipelines, RAG and Graph-RAG architectures, transformer models, LLMOps/MLOps platforms, and Python inference services. The role also owns model deployment, statistical modeling, responsible AI governance, state and memory systems, continuous retraining, innovation research, and stakeholder management.

Develops and maintains data engineering solutions involving ingestion, aggregation, processing, curation, modeling, and analysis of high-volume data. Uses Cloudera, Databricks, AWS, Python, PySpark, SQL, and related tools to unify data assets, generate insights, support business intelligence, and enable fraud, security, and digital identity products. Partners with product, data science, platform, technology, client, and sales teams to deliver data solutions, prototypes, and enhancements.

Build and maintain large-scale backend data systems, batch and streaming pipelines, datamarts, and cloud data workloads. Develop AI-powered agents for anomaly detection and workflow automation, using Scala, Python, Java, Spark, Cloudera, Databricks, and AWS. Support platform reliability, data integrity, downstream analytics, documentation, Agile collaboration, and team mentoring.

Designs and deploys generative AI and machine learning solutions for AML transaction monitoring. Builds RAG, MCP, tool-calling, LLM fine-tuning, multimodal content generation, computer vision, evaluation, observability, and MLOps systems. Develops scalable cloud solutions using AWS, Azure, and Databricks, integrates models into production applications, and ensures performance, security, and compliance. Collaborates with technical and business stakeholders while applying current AI/ML research.

Build data engineering solutions that ingest, aggregate, unify, and analyze high-volume data from diverse sources. Develop data pipelines and models using Cloudera, Databricks, AWS, Hadoop, Python, and SQL. Partner with product, data science, platform, technology, client, and sales teams to deliver actionable insights, prototypes, business intelligence, and innovative cyber, fraud, security, and digital identity products.

Own the strategy, roadmap, requirements, and delivery of AI-powered financial reconciliation products. Lead discovery with financial stakeholders, translate reconciliation and regulatory needs into BRDs, epics, and user stories, prioritize backlogs, and guide engineering and AI/ML teams from concept through launch. Define performance KPIs, support pricing and go-to-market activities, and ensure compliance, governance, auditability, and financial controls across enterprise reconciliation workflows.

Lead the design and development of scalable cloud-native data platforms and batch or real-time pipelines supporting AI, machine learning, and Generative AI. Build infrastructure for feature engineering, model training, inference, LLMs, RAG, embeddings, and vector databases. Ensure data quality, governance, security, observability, reliability, and cost efficiency while enabling MLOps and AgenticOps capabilities across enterprise AI initiatives.

Build and maintain scalable full-stack, data-intensive applications for Mastercard’s experimentation platform. Develop frontend, backend, APIs, and data-processing layers using Java, Spring Boot, TypeScript, React, Databricks, and Spark. Contribute to architecture, system design, testing, code reviews, performance optimization, and operational excellence. Collaborate with Product, Data Engineering, QA, and global development teams to deliver analytics-driven solutions, mentor colleagues, and improve engineering quality and scalability.

Supports the design, requirements, testing, documentation, and delivery of AI-powered and API-enabled financial reconciliation products. Maintains backlogs, coordinates agile execution, analyzes API telemetry and product metrics, supports AI feature evaluation, and documents controls for agentic workflows. Collaborates with engineering, operations, finance, compliance, and business stakeholders on discovery, integrations, client onboarding, go-to-market materials, roadmap planning, and leadership reviews.

Lead the architecture, development, scaling, and modernization of Java-based AWS microservices and full-stack web interfaces. Build RESTful APIs, responsive UIs, automated testing pipelines, telemetry, and continuous delivery workflows. Optimize distributed systems for resilience, scale, and low latency while driving architecture discussions, code reviews, technical sizing, and mentorship across the engineering organization.

Lead design and delivery of modern, secure, scalable cloud-native data platforms (Lakehouse) across AWS and Azure. Drive DevOps/IaC, data governance, medallion architecture, performance and cost optimization, mentor global engineers, and collaborate with stakeholders to translate business requirements into production data solutions.

Lead design and build cloud-native batch and real-time data platforms. Develop scalable ETL/ELT and streaming pipelines (Spark, Kafka, Flink), ensure data quality, governance, observability, security, cost optimization, and drive platform engineering excellence and team mentorship.

Designs, automates, and optimizes enterprise Windows platforms using SCCM, Ansible, PowerShell, and Infrastructure as Code. Responsibilities include OS deployment, patching, software distribution, compliance enforcement, server build pipelines, CI/CD integration, self-service delivery, troubleshooting, capacity planning, and platform modernization. The role leads Wintel engineering workstreams, establishes automation standards, collaborates with infrastructure and security teams, and supports scalable, secure, and highly available services.

Modernize security operations by building SOAR playbooks, AI-augmented investigation workflows, prompt libraries, Jupyter notebooks, response tooling, and integrations across SIEM, EDR, identity, ticketing, threat intelligence, and AI platforms. Partner with SOC and DFIR teams to automate high-effort processes, support major incidents, optimize response outcomes, maintain reusable tooling, and contribute to product roadmaps and technical documentation.

Design, build, and deploy AI and Generative AI solutions using LLMs, agent frameworks, and RAG. Develop scalable AI/ML pipelines, cloud-native integrations (Azure/Databricks/AWS), and MLOps/LLMOps deployments. Integrate AI with APIs, databases, and internal apps. Implement Responsible AI practices (bias detection, hallucination mitigation, explainability, safety guardrails). Monitor, test, and maintain production AI systems while staying current on Generative AI trends.

Lead and grow agile engineering teams to design, build, and operate large-scale, distributed payment systems. Provide hands-on technical leadership across architecture, code quality, testing, CI/CD, security, and cloud-native deployments while partnering with product and program teams to ensure predictable, high-quality delivery.

Design, build, automate, and optimize Mastercard/Vocalink's Windows platform using SCCM, Ansible, and IaC. Implement automated OS deployment, patching, compliance, and build pipelines; integrate with CI/CD and Git workflows. Lead platform modernization, troubleshoot complex Windows issues, and collaborate with security, infrastructure, application teams and vendors to deliver scalable, secure platform services.

Develop and maintain secure, high-quality software components for Mastercard’s unified UI platform. Responsibilities include coding, testing, deployment, troubleshooting, refactoring, automation, API integration, release pipeline orchestration, and architecture understanding. The engineer will contribute to Agile processes, conduct demos, improve operational metrics, promote engineering standards, review code, mentor team members, and collaborate with product owners to deliver reliable, accessible customer experiences.

Leads hands-on design, development, testing, documentation, deployment, and support of secure, scalable Java backend and microservices solutions. Collaborates with architecture, product, security, platform, QA, and cross-functional Agile teams; drives technical decisions, code quality, testing coverage, production support, root-cause analysis, and continuous improvement. Mentors junior engineers and contributes to engineering standards and knowledge sharing.

Administer and support AWS cloud and Windows Server environments, including monitoring, patching, vulnerability remediation, security hardening, troubleshooting, root cause analysis, automation with PowerShell, documentation, and infrastructure modernization or migration initiatives.