Top Tech Jobs & Startup Jobs in Chennai

Designs and maintains scalable ETL pipelines, data models, databases, and cloud-based analytics workflows. Manages PostgreSQL, MySQL, MongoDB, and Google Cloud SQL environments, performs database optimization, develops AutoML solutions, and deploys data engineering pipelines to production. Works within Agile teams using Scrum or Kanban, participating in sprint planning, backlog grooming, and daily stand-ups.

Maintains and develops PCB component libraries, including schematic symbols and geometry cells. Creates, reviews, modifies, and approves library components while supporting design for manufacturing and compliance with electrical design and quality processes. Collaborates with local and remote engineering teams, executes assigned tasks on schedule, protects confidential information, and continuously develops technical expertise. The role also supports automotive PCB design and may involve high-speed simulation and electronic circuit design.

Designs and operates scalable batch and streaming data pipelines, ETL/ELT frameworks, data models, APIs, and cloud-native services supporting ADAS and autonomous-driving solutions. Ensures data quality, security, governance, observability, and reliability across AWS or Azure platforms. Partners with AI/ML and MLOps teams to deliver production-ready datasets and features, while contributing to architecture, CI/CD, infrastructure automation, technical standards, and mentoring.

Build and operate AWS-based MLOps platforms for autonomous-driving workloads. Maintain highly available multi-zone environments, distributed multi-GPU Ray training clusters, and infrastructure for compute, storage, networking, and security. Develop Airflow and MLflow pipelines, support GitHub-based CI/CD for ML code, models, and infrastructure, troubleshoot platform issues, and ensure workflows are reproducible, traceable, and auditable.

Design, build, test, and operate cloud-based data ingestion and ETL systems. Develop backend services in Golang (with Python where appropriate), run workloads on Kubernetes/AWS, troubleshoot distributed systems, and collaborate to improve scalability, reliability, and performance.

Build, operate, and maintain MLOps platforms on AWS for autonomous driving workloads. Implement multi-zone, highly available training and deployment environments, operate distributed multi-GPU training (Ray), and maintain ML pipelines (Airflow, MLflow). Support CI/CD for ML code and infrastructure, ensure reproducible, traceable, auditable ML workflows, and troubleshoot platform-level issues.

Build and operate MLOps platforms on AWS for autonomous-driving ML workloads. Maintain highly available multi-zone training and deployment environments, run distributed multi-GPU training (e.g., Ray), implement Airflow orchestration and MLflow model lifecycle, support GitHub-based CI/CD for code/models/infrastructure, and ensure reproducible, traceable, auditable ML workflows.

Lead MLOps platform and infrastructure for autonomous-driving ML: build multi‑AZ AWS/Kubernetes environments, operate distributed multi‑GPU training, implement Airflow/MLflow pipelines, CI/CD, data ingestion/validation, monitoring, and collaborate with ML teams to deliver safety‑critical production ML systems.