Top Tech Jobs & Startup Jobs in Chennai

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.

Design and maintain scalable ETL/ELT pipelines, data warehouses, and big data infrastructure. Optimize databases and workflows, implement data quality and governance frameworks, automate testing and deployment, troubleshoot complex data issues, and ensure reliable systems. Collaborate with data scientists, analysts, and business teams to support analytics and machine learning. Mentor junior engineers and establish data engineering best practices.

Owns end-to-end delivery governance for technical workstreams, including timelines, scope, milestones, dependencies, risks, blockers, and stakeholder reporting. Drives delivery improvements through metrics such as velocity, throughput, and defects while coordinating Product, Business Analysis, Engineering, distributed teams, and vendors. Requires strong project management, Agile, planning, risk management, communication, and multitasking skills, along with experience leading IT delivery, data engineering, or analytics programs.

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.

Design, develop, test, deploy, monitor, and maintain secure, scalable software components and services. Own complex features end to end, shape architecture and APIs, implement CI/CD and quality automation, diagnose production issues, and improve observability and reliability. Lead risk-based testing across multiple levels, apply governed AI-assisted development, integrate LLM and agentic capabilities where valuable, mentor engineers, conduct technical reviews, and promote engineering standards and continuous delivery improvements.

Lead endpoint engineering and operations for a dual UEM environment (MECM/SCCM + Intune). Own patching, client health, vulnerability remediation, OS deployment, automation with PowerShell, incident/ITSM participation, and pilot AI-assisted reliability improvements while ensuring security and compliance.

Lead ETL and data warehouse testing efforts using SQL, Python, automation frameworks and AI-assisted tools. Validate ETL/ELT processes, perform data reconciliation and lineage analysis, develop automated test frameworks, integrate tests into CI/CD, mentor junior QA, and collaborate with engineers to resolve defects and improve data quality.

Design, develop, test, deploy, monitor, and maintain full-stack production software using Java, Python, APIs, services, data workflows, and cloud-native components. Build automated testing and CI/CD quality gates, apply secure and maintainable engineering practices, improve observability and operational reliability, modernize legacy systems, and collaborate across product, architecture, security, data, and operations teams. Use approved AI-assisted development tools responsibly throughout the software delivery lifecycle.

Lead AI/ML engineering vision and roadmap for healthcare and life sciences. Oversee multiple client engagements, design and deploy scalable production-grade AI solutions (ML, NLP, deep learning, computer vision, LLMs), ensure regulatory readiness, responsible AI practices, and drive adoption and value realization while managing executive client relationships and expanding AI adoption.