Top AWS Jobs in Chennai

Develop and deploy scalable backend services for web applications. Responsibilities include RESTful API development and testing, database integration, asynchronous task queuing, containerization, and collaboration with the development team. The role uses Python frameworks such as FastAPI, Flask, or Django, relational databases, Docker, and potentially Kubernetes and cloud platforms.

Develop and deploy scalable full-stack enterprise applications using .NET, C#, Python, React or Angular, and multi-cloud platforms. Design APIs, microservices, event-driven integrations, and practical AI features using Generative and Agentic AI frameworks. Support containerization, infrastructure as code, CI/CD, performance, security, testing, and cloud-native implementation across AWS, Azure, and Google Cloud. Collaborate with architects, DevOps, product, and infrastructure teams while contributing to solution design, mentoring, and hands-on coding.

Leads enterprise knowledge engineering and AI initiatives, designing ontologies, knowledge graphs, semantic layers, and graph-based machine learning solutions. Owns architecture and delivery for complex client programs, develops teams, drives innovation and thought leadership, and advises senior stakeholders. Responsibilities include integrating knowledge systems with data platforms, building AI and LLM pipelines, applying NLP and search techniques, shaping sales and pre-sales efforts, and establishing standards for scalable knowledge graph solutions.

Leads enterprise knowledge engineering and knowledge graph initiatives, designing ontologies, semantic models, AI solutions, and data pipelines. Owns architecture and delivery for complex client programs, applies graph-based machine learning and LLM technologies, and establishes standards for knowledge model governance. Builds executive stakeholder relationships, supports sales and presales, develops reusable intellectual property, leads teams, mentors engineers, and drives innovation across AI, semantic technologies, and agentic systems.

Design and lead scalable data and AI/ML architectures for enterprise applications. Responsibilities include defining data requirements, modeling storage and integration structures, developing and deploying machine learning and generative AI solutions, building data and MLOps pipelines, and applying cloud, distributed computing, and high-performance computing technologies. The role also involves stakeholder collaboration, technical leadership, mentoring, architecture standards, and delivering production-ready solutions in a consulting environment.

Design and lead scalable data architectures, including data modeling, storage, integration, and cloud-based platforms. Develop and operationalize AI and machine learning solutions, data pipelines, and MLOps workflows using Python and related frameworks. Collaborate with technical and business stakeholders, establish architecture standards, solve complex data problems, mentor team members, and support production deployments involving deep learning, generative AI, distributed computing, and large language models.

Leads end-to-end knowledge engineering and AI solutions for complex client programs. Designs ontologies, semantic models, knowledge graphs, data architectures, and graph-based machine learning solutions. Oversees data pipelines for AI and LLM applications, establishes knowledge graph standards, advises senior stakeholders, supports sales and presales, develops thought leadership, and leads engineering teams. Requires extensive experience with Azure, graph databases, Python, cloud platforms, machine learning frameworks, and enterprise-scale semantic and AI technologies.

Designs and leads scalable data architectures, including data modeling, storage, integration, and cloud-based platforms. Develops and operationalizes AI/ML solutions, data pipelines, and MLOps processes using large-scale datasets, machine learning frameworks, and generative AI technologies. Collaborates with technical and business stakeholders, establishes architecture standards, mentors team members, and provides solutions across multiple teams. Requires strong programming, distributed computing, cloud, data engineering, and client-facing consulting skills.

Build AWS-based knowledge engineering and AI solutions, including knowledge graphs, ontologies, semantic layers, data pipelines, vector search, LLM grounding, and graph-based applications. The role involves designing, developing, testing, integrating, and deploying scalable components using services such as Neptune, Glue, Lambda, Bedrock, and SageMaker. Candidates collaborate with client and delivery teams, document technical findings, support solutioning and demonstrations, and mentor junior engineers while remaining hands-on.

Define and lead scalable data architectures, including data modeling, storage, integration, and cloud platforms. Develop and operationalize AI and machine learning solutions using deep learning, generative AI, and large language models. Build data, DevOps, and MLOps pipelines; collaborate with business and technical stakeholders; establish architecture standards; solve complex client problems; and mentor team members.

Leads enterprise knowledge engineering and knowledge graph programs, designing ontologies, semantic models, AI solutions, graph-based ML models, and data pipelines. Owns architecture, standards, innovation, client advisory, presales, and delivery for complex transformation programs. Builds and mentors engineering teams, partners with senior stakeholders, and advances LLM, agentic AI, NLP, and semantic technologies.

Design and implement scalable data architectures, AI/ML models, data pipelines, and cloud-based production systems. Responsibilities include data modeling, storage and integration, deep learning and generative AI applications, distributed computing, MLOps, stakeholder collaboration, technical leadership, and mentoring.

Designs and builds AWS-based data architecture and knowledge engineering solutions, including knowledge graphs, ontologies, semantic layers, AI pipelines, vector search, and LLM grounding. Develops scalable components using Neptune, S3, Glue, Lambda, SageMaker, Bedrock, and related services. Responsibilities include graph ingestion, schema and ontology management, data integration, testing, deployment, documentation, client collaboration, and technical knowledge sharing. The role requires hands-on Python, data engineering, graph database, NLP, machine learning, and cloud experience.

Designs, builds, tests, and deploys AWS cloud platform solutions integrating cloud and traditional infrastructure. Develops architecture proofs of concept, ensures security and performance, manages scalable environments, troubleshoots infrastructure issues, evaluates new cloud technologies, and mentors team members while contributing to cross-team decisions.

Own the Market Data product roadmap, prioritizing problems, features, scope, requirements, data coverage, vendors, and quality standards. Collaborate with engineering, analysts, testing, sales, support, and clients to deliver data platform capabilities. Identify coverage gaps, validate lead times, assess commercial impact, document modules, explain platform behavior, support issue resolution, and translate client challenges into future product opportunities.

Designs scalable data architectures and develops secure ETL/ELT pipelines using Databricks, Delta Lake, DLT, Auto Loader, PySpark, and cloud platforms. Responsibilities include data modeling, Unity Catalog governance, PII masking, Delta Sharing, BI integrations, SQL dashboards, GenAI dataset preparation, CI/CD deployment, and performance optimization. The role collaborates with stakeholders to deliver reliable, observable, and cost-efficient data solutions.

Review and integrate application and technical architecture requirements across security, integration, performance, quality, and operations. Design and implement Databricks data ingestion and end-to-end pipelines from multiple sources using cloud platforms, ETL, data engineering, and programming languages such as Python, PySpark, or Scala. Lead solution workshops, support RFP pursuits, engage clients, and coordinate multiple stakeholders while delivering data-driven technical solutions.

Designs and implements scalable data architectures, ETL/ELT pipelines, data models, storage, and integrations using Databricks. Builds secure DLT pipelines with Auto Loader, Delta Lake, Unity Catalog, PII masking, Delta Sharing, and medallion architecture. Develops Python and PySpark transformations, integrates BI tools, prepares GenAI datasets, and deploys pipelines through CI/CD. Responsibilities include performance tuning, observability, governance, cloud data engineering, and stakeholder-facing dashboards.

Designs application data architecture, storage, integration, and scalable Databricks ETL/ELT pipelines. Builds PySpark and Python transformations, Auto Loader and DLT workflows, Unity Catalog security controls, Delta Sharing, BI integrations, dashboards, and GenAI-ready datasets. Optimizes cloud-based data processing for cost and reliability while collaborating with stakeholders on data modeling, governance, and operational SLAs.

Designs and integrates technical and application architecture requirements across security, performance, quality, integration, and operations. Develops Databricks data ingestion and end-to-end pipelines from multiple sources, using Python, PySpark, or Scala in cloud environments. Provides data engineering and cloud architecture solutions, leads client workshops and RFP pursuits, and manages stakeholders while advising on infrastructure, software, networking, and security decisions.

Designs and implements scalable Databricks data architectures, including ETL/ELT pipelines, data modeling, ingestion, transformation, validation, governance, security, and integrations. Builds Delta Lake and DLT pipelines, configures Unity Catalog, enables data sharing, optimizes cloud workloads, and prepares datasets for analytics and generative AI. The role also supports BI integrations, CI/CD deployment, performance tuning, and collaboration with technical and business stakeholders.

Designs and governs scalable application data architectures, including storage, integration, modeling, and data warehousing. Builds Databricks ETL/ELT pipelines with Delta Lake, Auto Loader, and Delta Live Tables; implements Unity Catalog security, PII masking, data sharing, observability, and performance optimization. Develops PySpark and Python transformations, integrates BI tools, prepares AI-ready datasets, and deploys pipelines through CI/CD. Collaborates with stakeholders to deliver secure, reliable, and cost-efficient data platforms.

Designs and governs scalable application data architecture, including data models, storage, integrations, and cloud-based Databricks pipelines. Builds secure ETL/ELT workflows using Delta Lake, Auto Loader, DLT, Unity Catalog, and PySpark; implements data quality, PII masking, lineage, sharing, and performance optimization. Integrates BI tools, prepares GenAI datasets, and deploys pipelines through CI/CD. Requires strong Databricks, Python, cloud, data warehousing, and data engineering expertise.

Designs and implements scalable data architectures, ETL/ELT pipelines, and secure Databricks solutions. Responsibilities include Python and PySpark development, Delta Lake and DLT pipelines, Unity Catalog governance, cloud integration, data modeling, performance optimization, BI connectivity, GenAI dataset preparation, and CI/CD deployment. The role also supports data sharing, observability, security, and stakeholder-facing dashboards.

The Data Architect will define application data requirements, models, storage, and integrations. Responsibilities include building scalable Databricks ETL/ELT and DLT pipelines, implementing Unity Catalog security and governance, optimizing cloud data workloads, developing Python and PySpark transformations, integrating BI tools, preparing GenAI datasets, and deploying pipelines through CI/CD. The role also involves troubleshooting, performance tuning, data sharing, stakeholder collaboration, and ensuring reliable, secure, and cost-efficient data platforms.