385 Cloud Engineer jobs in Hyderabad

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TCS Hiring for skill " AWS Data Engineer".

Role: AWS Data Engineer (Developer)

Required Technical Skill Set: AWS Data Engineers with Python Programming skills, should know Pyspark and SQL having clinical background is recommended but not necessary. (Note: GCP or Azure, or other cloud skills are not acceptable. AWS & Python are Must)

Experience: 5+ Years

Work Location: Bangalore/Hyderabad/Bhubaneshwar

Desired Competencies:

Must-Have:

1. At least 4-5 years of hands-on experience in Snowflake /data lake technology stack
2. Strong understanding of data warehousing concepts, data modeling, metadata management
3. Must have experience in SQL performance measuring, query tuning, and database tuning
4. Must have experience in python.

Good-to-Have:

1. Good understanding of Agile software development principles including using common tools such as JIRA
2. Knowledge of Agile Methodologies.

Presentation skills with a high degree of comfort with both large and

1. small audiences

Strong communication and collaboration abilities in addition to

1. technical depth
2. Previous experience operations support
3. Good communication, Analytical & presentation skills.
4. Ability to work with cross functional teams and be a team player.

Ability to demonstrate professionalism, enthusiasm, and create a

1. collaborative climate.
2. Ability to work independently

Note: Applications will be shortlisted based upon response received & Eligibility criteria.

About the Role

Cloud Server admin is responsible to Monitoring Cloud infrastructure server & Cloud Security management, Managing Inventory, Vulnerability assessment Updating security patches & AV Cloud Accounts and User Administration & Billing and budget tracking user access permissions & auditing access rights Supporting projects for any technical issues.

Key Responsibilities:

• The primary job responsibilities of the cloud administrator are to work in coordination with the IT department to develop and support cloud, windows or Unix infrastructure.

• They usually provide technical assistance on windows, cloud-based systems, and resolve operational problems.

• The professionals will also assist in the administration of cloud service and a Windows server environment.

• Here are some of the additional cloud systems administrator responsibilities that come under the job

Responsibilities:

• Implement and manage compliance tools like eDiscovery and retention policies.

• User Support and Training? Provide technical support and guidance to end-users regarding Office 365 applications and services.

• Conduct training sessions and create documentation to enhance user adoption and productivity. • Remote support of on-site engineers during troubleshooting of tickets. Integration and Optimization

• Work with IT teams to integrate Office 365 with other applications and services.

• Implement and optimize automation using Microsoft Power Automate, Power Apps, and PowerShell scripts. Monitoring and Reporting

• Generate usage and performance reports to identify trends and recommend improvements.

• Monitor security logs and alerts and respond promptly to potential threats.

What You'll Need:

• Regularly updating assets and monitoring any new installation over network.

• Supporting user tickets for any escalations and monitoring pending tickets, Monitoring server availability and performance monitoring.

• Should be able to having good knowledge on AWS security concepts like WAF, Firewall, VPC, security groups, IAM access and Cloud trial etc.

• Should be having good knowledge on Azure concepts like NSG, Firewall, Azure Cloud Security, DevOps and other Azure services.

• Handling technical issues with Cloud storages, AWS & Azure management.

• Creating and managing cloud inventory and tracking the usage of all services.

• Attending webinars/trainings to improve technical skills and adopt latest technology.

• Responsible for designing, maintaining, scaling, and ensuring security for cloud services as per standards

• Supports internal auditors to ensure that regular audits are conducted and all reports are updated.

• Ensure self and team development Trouble shooting any technical issues and act accordingly.

• Coordinating with supporting tickets for any tech

• Backups & restores as per backup plans

• Having good technical knowledge on Azure AD, AD, Backup, AV, Storage & encryption concepts

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We are looking for a Mid-level Cloud Engineer with hands-on expertise in designing, automating, and operating production-grade cloud infrastructure. This role requires a strong background in AWS services, DevOps/DevSecOps practices, Infrastructure as Code, monitoring, and container orchestration. The engineer will be responsible for building resilient, secure, and scalable systems across multi-region environments.

Roles and Responsibilities:

• Design, deploy, and manage infrastructure using a broad set of AWS services including VPC, IAM, RDS, EC2, ECS, S3, Bedrock, CloudWatch, EFS, MQ, OpenSearch, ElastiCache, ECR, CodeArtifact, Lambda, Route53, CloudTrail, and ELB.
• Operate and scale multi-region, production-facing applications, ensuring high availability, fault tolerance, and DR readiness.
• Implement DevOps & DevSecOps practices with automation-first approaches for deployments, security, compliance, and monitoring.
• Manage Infrastructure as Code (IaC) using Terraform and Ansible.
• Leverage containerization & orchestration tools such as Docker, Docker Compose, and Kubernetes.
• Administer and optimize Linux systems (Ubuntu/Debian), focusing on performance, security, and automation.
• Build and maintain CI/CD pipelines with GitHub Actions (preferred) or Jenkins.
• Develop Python, Bash, and Shell scripts for automation, tooling, and system orchestration.
• Implement and manage network configurations including LAN, WAN, IPv4/IPv6, VPNs, site-to-site connectivity, and firewalls.
• Leverage monitoring and observability tools such as Grafana, AWS CloudWatch, and APM solutions (New Relic, Datadog – nice to have).
• Work with Airflow for workflow orchestration and data pipeline automation.
• Ensure compliance with SOC2 standards and manage cloud DR/backup strategies.
• Collaborate with cross-functional teams, providing technical guidance, troubleshooting, and performance optimization.
• Explore AI-assisted engineering tools (Codex, Cursor, GitHub Copilot) to improve development and automation efficiency.

Required Skills & Qualifications:

• 1-3 years of experience in DevOps, Cloud Engineering, or related fields.
• Proven track record of managing AWS services at scale in production environments.
• Expertise in Terraform, Ansible, and IaC practices.
• Strong Linux administration skills (Ubuntu/Debian preferred).
• Hands-on experience with Docker, Docker Compose, and Kubernetes for container orchestration.
• Proficiency in scripting languages (Python, Bash, Shell).
• Experience with CI/CD pipelines (GitHub Actions preferred).
• Knowledge of networking fundamentals (LAN, WAN, IPv4, IPv6, VPNs, Firewalls).
• Experience operating multi-node Elasticsearch & Kibana clusters.
• Strong problem-solving and troubleshooting abilities.
• Excellent communication and collaboration skills.

Nice to Have:

• Exposure to AI/ML-based productivity tools for DevOps workflows.
• Hands-on experience with APM tools such as New Relic or Sentry.
• Experience managing on-premise rack servers in hybrid environments.

We're looking for a highly skilled and experienced Cloud AI Engineer to join our dynamic team. In this role, you'll be instrumental in designing, developing, and deploying cutting-edge artificial intelligence and machine learning solutions leveraging the full suite of Google Cloud Platform (GCP) services.

Objectives of this role

• Lead the end-to-end development cycle of AI applications, from conceptualization and prototyping to deployment and optimization, with a core focus on LLM-driven solutions.
• Architect and implement highly performant and scalable AI services, effectively integrating with GCP's comprehensive AI/ML ecosystem.
• Collaborate closely with product managers, data scientists, and MLOps engineers to translate complex business requirements into tangible, AI-powered features.
• Continuously research and apply the latest advancements in LLM technology, prompt engineering, and AI frameworks to enhance application capabilities and performance.

# Responsibilities

• Develop and deploy production-grade AI applications and microservices primarily using Python and FastAPI, ensuring robust API design, security, and scalability.
• Design and implement end-to-end LLM pipelines, encompassing data ingestion, processing, model inference, and output generation.
• Utilize Google Cloud Platform (GCP) services extensively, including Vertex AI (Generative AI, Model Garden, Workbench), Cloud Functions, Cloud Run, Cloud Storage, and BigQuery, to build, train, and deploy LLMs and AI models.
• Expertly apply prompt engineering techniques and strategies to optimize LLM responses, manage context windows, and reduce hallucinations.
• Implement and manage embeddings and vector stores for efficient information retrieval and Retrieval-Augmented Generation (RAG) patterns.
• Work with advanced LLM orchestration frameworks such as LangChain, LangGraph, Google ADK, and CrewAI to build sophisticated multi-agent systems and complex AI workflows.
• Integrate AI solutions with other enterprise systems and databases, ensuring seamless data flow and interoperability.
• Participate in code reviews, establish best practices for AI application development, and contribute to a culture of technical excellence.
• Keep abreast of the latest advancements in GCP AI/ML services and broader AI/ML technologies, evaluating and recommending new tools and approaches.

# Required skills and qualifications

• Two or more years of hands-on experience as an AI Engineer with a focus on building and deploying AI applications, particularly those involving Large Language Models (LLMs).
• Strong programming proficiency in Python, with significant experience in developing web APIs using FastAPI.
• Demonstrable expertise with Google Cloud Platform (GCP), specifically with services like Vertex AI (Generative AI, AI Platform), Cloud Run/Functions, and Cloud Storage.
• Proven experience in prompt engineering, including advanced techniques like few-shot learning, chain-of-thought prompting, and instruction tuning.
• Practical knowledge and application of embeddings and vector stores for semantic search and RAG architectures.
• Hands-on experience with at least one major LLM orchestration framework (e.g., LangChain, LangGraph, CrewAI).
• Solid understanding of software engineering principles, including API design, data structures, algorithms, and testing methodologies.
• Experience with version control systems (Git) and CI/CD pipelines.

Preferred skills and qualifications

Bachelor's or Master's degree in Computer Science

Good to have:

Experience with MLOps practices for deploying, monitoring, and maintaining AI models in production.

Understanding of distributed computing and data processing technologies.

Contributions to open-source AI projects or a strong portfolio showcasing relevant AI/LLM applications.

Excellent analytical and problem-solving skills with a keen attention to detail.

Strong communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders