Top AWS Jobs in Chennai

Leads the technical design, development, and evolution of enterprise sales and marketing data platforms. Builds scalable Snowflake and dbt pipelines, defines data models and reliability standards, drives CI/CD and DevSecOps practices, mentors engineers, partners with stakeholders, and leads complex initiatives from discovery through production. The role also conducts incident analysis, reduces technical debt, and evaluates AI-assisted engineering technologies.

Supports the design, development, testing, deployment, and maintenance of cloud-based software in a microservices environment. Works with APIs, telemetry, structured and unstructured data, distributed systems, and ML/AI algorithms. Assists with automation, code reviews, refactoring, documentation, CI/CD, testing, and software integration while collaborating with product teams and stakeholders.

Leads quality control engineering for piping, rotary, static, and packaged equipment in oil and gas, petrochemical, chemical, and power facilities. Reviews vendor quality documents, inspection procedures, NDE and welding documentation, conducts pre-inspection meetings, coordinates shop inspections and corrective actions, approves inspection releases and manufacturing data books, and supervises, mentors, and trains QC engineers. The selected candidate must initially work in an overseas office for two years.

Coordinates engineering and vendor data collection for process plant EPC projects. Validates data for consistency, correctness, and completeness; imports and exports data using client templates; reviews comments and coordinates corrections; tracks issues; prepares clarification, progress, and error reports; and supports data management activities. The role also requires knowledge of process plant IT systems, databases, cloud services, cybersecurity, engineering drawings, tag conventions, and CFIHOS standards.

Architect and build resilient platform services, APIs, shared frameworks, developer tooling, and automated workflows for enterprise-scale software. Lead technical spikes, code reviews, refactoring, performance optimization, architecture documentation, and mentorship. The role requires backend expertise in Java, Python, or Go; distributed systems and microservices experience; API development; and observability skills. Preferred experience includes AWS, infrastructure as code, CI/CD, Docker, Kubernetes, GitOps, internal developer platforms, and enterprise security frameworks.

Design and build predictive and prescriptive models (churn, segmentation, survival, forecasting), run experiments (A/B, multi-armed bandit), deploy cloud-based solutions (PySpark on AWS/GCP), synthesize insights, and collaborate cross-functionally to inform marketing, finance, and operations decisions.

Develops and deploys machine learning models, performs exploratory and statistical analysis, builds data pipelines, conducts experiments, and communicates insights to technical and business stakeholders. Owns the end-to-end model lifecycle, including feature engineering, deployment, and monitoring. Collaborates across product, engineering, and business teams, applies tools such as MLOps platforms and cloud services, and mentors junior data scientists or analysts.

Designs, builds, and maintains scalable batch and real-time data pipelines, warehouses, lakes, and models supporting analytics and machine learning. Responsibilities include data quality monitoring, database optimization, cloud infrastructure management, governance, security, CI/CD, documentation, and production troubleshooting. The role collaborates with data scientists, analysts, and product teams to deliver reliable datasets and infrastructure.

Analyzes healthcare business requirements, translates them into user stories and functional specifications, and develops end-to-end software solutions. Manages stakeholder communication, validates workflows and architecture, supports product roadmaps, and collaborates with development, testing, and technical teams. The role requires strong knowledge of US healthcare payer operations, analytics, Agile and Waterfall methodologies, documentation, presentation, SQL, and data technologies. It also mentors junior team members and helps identify sellable product features.

Design, develop, and optimize data pipelines using PySpark and SQL; manage Snowflake data warehouses; process large-scale data through the Hadoop ecosystem; and ensure data quality, governance, performance, and reliability. The role also automates workflows, uses ETL and orchestration tools, and collaborates with data scientists, analysts, and business stakeholders.

Designs, develops, and maintains scalable software systems for the Appian platform. Leads architecture and design, establishes code quality standards, builds developer tooling, mentors engineers, drives features from requirements through deployment, resolves production issues, and optimizes performance. The role also involves distributed systems, cloud-native technologies, AI integration, technical initiative leadership, and collaboration with product stakeholders.

Develops, maintains, and supports software solutions for ICIS products and services. Responsibilities include troubleshooting customer issues, contributing to product planning and estimation, supporting other teams, participating in knowledge sharing, and driving continuous improvement. The role requires strong C# and .NET expertise, familiarity with React, TypeScript, micro frontends, Docker, MongoDB, AWS, Python, GitHub, CI pipelines, and Agile development.

Build, deploy, and manage end-to-end AI services for traditional and generative AI use cases. Design scalable AI/ML pipelines, distributed computing systems, and model-serving solutions. Develop and optimize deep learning and generative AI algorithms, including real-time and multimodal models. Work with cloud AI platforms, containerization, Kubernetes, CI/CD, and large-scale training systems while collaborating with cross-functional teams.

Provides cloud and server infrastructure consulting for large enterprise environments. Designs and implements AWS and Azure solutions, manages data centers and cloud platforms, resolves operational issues, applies security and patching practices, validates billing, and develops infrastructure roadmaps. Requires expertise in cloud architecture, Linux and Windows administration, networking, backup and recovery, FinOps, ITIL processes, automation, and stakeholder communication.

Manage and implement large-scale AWS, Azure, on-premises server, data center, and cloud environments. Responsibilities include cloud infrastructure deployment, server provisioning, patching, security hardening, vulnerability remediation, monitoring, backup and recovery, networking, DNS, databases, CDN, IAM, FinOps, and troubleshooting. The role also develops cloud automation and deployment strategies, applies ITIL processes, resolves complex technical issues, and advises clients on scalable, secure, and financially sound infrastructure solutions.

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.

Lead the architecture, design, development, deployment, and maintenance of scalable backend systems, distributed services, and mission-critical REST and GraphQL APIs. Provide hands-on technical guidance, mentor engineers, establish coding standards, oversee code quality, and drive end-to-end delivery. Design asynchronous pipelines, microservices, database solutions, cloud deployments, CI/CD processes, observability, and secure cloud-native architectures on AWS.

Develop and operate backend systems using Java, Python, AWS, microservices, databases, queues, and event-driven architectures. Responsibilities include designing scalable services, integrating third-party APIs, improving performance and observability, managing production Docker deployments, conducting incident reviews, writing architecture documentation, mentoring engineers, and owning systems through launch, on-call, and ongoing optimization. The role also requires daily use and review of AI-assisted coding tools.

Lead the design and development of scalable full-stack web applications and distributed systems, guiding engineering teams and ensuring best practices in software development.

Provide L1-L3 production support for enterprise MongoDB Atlas environments, including monitoring, scaling, backups, upgrades, performance tuning, security, networking, disaster recovery, and incident resolution. Build Terraform-based infrastructure automation, support multi-cloud deployments across Azure, AWS, and GCP, partner with application and engineering teams, coordinate vendor escalations, and participate in 24/7 on-call coverage.

Build production AI/ML systems for extracting structured data from complex documents. Responsibilities include document ingestion, OCR, confidence modeling, RAG knowledge bases, agentic workflows, human-in-the-loop review, reprocessing and diffing, model optimization, evaluation, monitoring, and governance. The role also provides technical mentorship to mid-level AI engineers and owns systems end to end.

Develop and lead end-to-end features for scalable cloud-native SaaS applications using ReactJS, TypeScript, NodeJS, GraphQL, and modern cloud technologies. Translate customer and business needs into technical solutions, troubleshoot complex issues, mentor engineers, improve development processes, and guide adoption of emerging technologies, including AI-driven user experiences and workflow automation.

Designs and develops scalable automated testing solutions for carrier-grade, cloud-native network products. Responsibilities include building functional, regression, performance, and end-to-end test frameworks; developing tests with Python, Java, or Go; integrating testing into CI/CD pipelines; applying AI/ML for test generation and defect analysis; analyzing network and test data; and collaborating with development, product, and QA teams.

Designs and develops scalable automated testing solutions for carrier-grade, cloud-native network products. Responsibilities include building functional, regression, performance, and end-to-end test suites; integrating testing into CI/CD pipelines; applying AI/ML for test generation, anomaly detection, defect prediction, and self-healing automation; analyzing network and test data; and collaborating with development, product, and QA teams.

Build and productionize customer data and AI solutions on Databricks. Own architecture, design, coding, deployment, and integration of data pipelines, ML/AI models, and applications. Lead customer technical projects, guide architecture decisions, manage delivery scope and timelines, train stakeholders, resolve implementation issues, and create reusable frameworks. Collaborate with engineering, product, project management, support, and customer teams while traveling to customers approximately 20% of the time.