Top AWS Jobs in Chennai

Designs, develops, and optimizes scalable data pipelines and structures for analytics and downstream apps. Implements SQL transformations, ingestion frameworks, cloud data solutions (Snowflake, Databricks), ensures data quality, lineage, and governance, and mentors junior engineers while improving internal tools and automation.

Lead software engineering for distributed systems within United AI Studio. Responsibilities include designing, implementing, testing, deploying, and operating software; conducting code reviews; solving complex problems; mentoring engineers; and collaborating across teams. Required experience includes Java with Spring Boot, REST APIs, Python, cloud platforms, service-oriented development, software development lifecycle practices, and strong computer science fundamentals. Preferred experience includes databases, Terraform, CI/CD infrastructure, distributed teams, architecture, and AI/ML projects.

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.

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.

Lead the delivery of AI products across international markets by coordinating internal engineers, contractors, and vendors. Establish engineering standards, make technical decisions, review code, ensure testing and release readiness, and guide solutions from proof of concept through enterprise production. Oversee data, AI, generative AI, agentic AI, and full-stack applications while managing dependencies, technical debt, and delivery quality. Collaborate with product, engineering, data science, security, and global stakeholders, and evaluate emerging technologies.

Build and operate production software and AI systems across cloud infrastructure. Responsibilities include developing APIs and services, operating RAG and inference platforms, implementing CI/CD and observability, managing AWS or GCP environments with Kubernetes and Terraform, optimizing model serving and GPU usage, controlling cloud and AI costs, and partnering with stakeholders on reliability, security, governance, and technical tradeoffs.

Seeking a DevOps engineer to design CI/CD pipelines, manage cloud infrastructure, automate deployments, and troubleshoot issues in a collaborative environment.

Build, deploy, monitor, and retrain machine learning models for ETA forecasting, status extraction, and logistics automation. Develop regression, classification, time-series, NLP, and LLM-based solutions using noisy supply chain data. Own production ML pipelines, diagnose offline-to-live performance gaps, and measure business impact. Collaborate with product, engineering, and operations teams while mentoring data scientists and making architecture and build-versus-buy decisions.

Lead data science efforts to design, develop, and productionize ML models, services and scalable data pipelines. Collaborate across lines of business to deliver AI-powered software solutions, perform analysis and insights, and optimize business outcomes in a secure, scalable financial-services environment.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Design and implement full-stack features with minimal guidance. Collaborate with PMs, UX, QA, and engineers. Build frontend components (React/Angular) and backend services (Java/Kotlin/.NET). Develop and consume RESTful APIs, use Git, Gradle/Maven, and troubleshoot production issues. Participate in code reviews, follow architectural patterns, and support delivery during critical phases.

Lead the design, implementation, and operation of AI Ops and SRE solutions in public cloud environments. Build RAG pipelines, agentic workflows, and AI-powered enterprise applications; automate infrastructure with Terraform and GitHub Actions; manage Kubernetes; lead incident response; ensure reliability, scalability, and performance; establish AI evaluation and monitoring frameworks; and mentor engineering teams.

Develop and operate reliable cloud infrastructure and applications, automate cloud operations and deployments, implement infrastructure procedures, and provide 24/7 rotating on-call support. The role requires enterprise cloud administration across public-cloud platforms, programming, CI/CD, Kubernetes or Terraform experience, and knowledge of cloud security, networking, monitoring, logging, and Linux/Windows systems.

Build and deploy AI-powered customer solutions across discovery, prototyping, and production. Responsibilities include designing secure multi-tenant and hybrid architectures, integrating identity and access management, encryption, networking, data engineering, CI/CD, observability, and production support. The role partners directly with enterprise customers, executive sponsors, and cross-functional teams to solve ambiguous problems, demonstrate prototypes, and standardize successful solutions. Regular domestic and occasional international travel is required.

Lead QA strategy and roadmap for AI and SaaS platforms. Design and implement scalable automation frameworks, validate AI/ML workflows, perform performance/security/regression testing, ensure end-to-end coverage across microservices, APIs, mobile and web in multi-tenant cloud, and document QA processes while partnering with engineering and data science teams.

Build and scale GitLab’s partner-sourced and influenced revenue in India across hyperscalers, system integrators, cloud-native partners, and AI-native partners. Own strategic relationships, AWS/GCP Marketplace motions, joint business plans, QBRs, partner enablement, pipeline generation, bookings, and quota attainment. Collaborate with sales, marketing, and APJ ecosystem teams while helping shape India’s partner coverage model and transitioning relationships from transactional to strategic.

The Staff Infrastructure Security Engineer will lead security initiatives, set architectural patterns, conduct security reviews, and mentor engineers while ensuring security in cloud infrastructures for GitLab.

Leads the strategic architecture, lifecycle planning, security design, and modernization of Gartner’s global enterprise, hybrid-cloud, campus, data center, and event networks. Defines reference architectures, roadmaps, standards, and automation strategies; designs routing, switching, wireless, load balancing, Palo Alto security, SASE, SD-WAN, AWS, and Azure connectivity. Acts as the highest escalation point for complex infrastructure issues, partners with cross-functional technology teams, and mentors network engineering teams.

Provides L2 technical support for production and non-production environments, resolving complex application, infrastructure, database, and integration incidents. Responsibilities include monitoring system health, conducting root cause analyses, managing production changes and rollbacks, mentoring L1 support, improving runbooks and automation, and evaluating AI-assisted observability and support tools. The role collaborates with global engineering, product, and business teams to maintain platform stability and reduce incident resolution times.

Design and implement enterprise-grade Generative AI and LLM architectures, lead Azure OpenAI and AWS Bedrock integrations, ensure API-based system integration and application security, drive AI/ML DevOps, support RFPs, and mentor teams in prompt engineering.

Provides cloud security consulting and governance across AWS, Azure, and GCP environments. Performs risk assessments, control validation, QA reviews, evidence gathering, and security approval evaluations. Maintains cloud control libraries, processes, procedures, and playbooks while tracking governance metrics and reporting outcomes. Supports high-volume policy evaluations, identifies automation opportunities using Python, and ensures cloud controls align with enterprise policies, regulatory requirements, and security frameworks.

Build, operate, and continuously improve a centralized observability platform on Kubernetes and AWS. Develop CI/CD pipelines, automate infrastructure and workflows, support GitOps and self-service capabilities, troubleshoot platform issues, and participate in on-call operations. Enable company teams to adopt observability through OpenTelemetry, standards, best practices, and internal collaboration. The role requires strong Apache Kafka experience and familiarity with cloud, container orchestration, monitoring, and DevOps tools.

Design, build, operate, and maintain Apache NiFi dataflows for application, system, and data integrations. Responsibilities include transformation, routing, enrichment, orchestration, security, monitoring, troubleshooting, performance tuning, high availability, versioning, and lifecycle management. The engineer provides on-call third-level support, ensures governance and auditability, and coaches user-team developers on NiFi usage. The role also involves consulting with clients to translate business needs into effective technology and integration solutions.

Build, operate, and improve a centralized observability platform on Kubernetes and AWS. Develop CI/CD pipelines, automate infrastructure using Terraform and GitOps, support Grafana-based monitoring, promote OpenTelemetry standards, troubleshoot platform issues, and participate in on-call operations. Collaborate with developers and internal teams to optimize workflows, deploy services, and improve observability adoption. The role requires strong Apache Kafka experience, DevOps expertise, cloud knowledge, and excellent English communication.

Designs, builds, and optimizes scalable data pipelines, ETL/ELT processes, integrations, and cloud data platforms. Ensures data quality, governance, observability, reliability, and efficient processing for analytics and AI/ML initiatives. Supports Meridian platform implementation, troubleshoots production issues, automates workflows, and collaborates with architects, analysts, product owners, and stakeholders. Provides technical mentorship, participates in architecture and code reviews, and promotes engineering best practices.