Top Artificial Intelligence Jobs in Chennai

As a Software Engineer III, you will design and implement software solutions, ensuring quality and stability, while analyzing data and contributing to team culture.

The AI Engineer is responsible for designing, validating, and supporting engineering processes, ensuring compliance with development plans and customer requirements.

Lead and grow the Applied AI team, providing engineering leadership and product guidance for AI agents in physical operations. Drive team performance and collaborate with cross-functional teams to ensure product reliability and user value delivery.

Lead AI/ML engineering teams to build AI solutions, overseeing strategy, architecture, and high-performance team management. Collaborate with other teams and ensure operational excellence in AI projects.

The Engineer-ECM will manage ECOs, use Agile for change orders, and support product updates while ensuring process efficiency and team training.

Design and implement AI workflows, develop APIs, collaborate with teams, deploy cloud models, and ensure responsible AI practices.

The role involves designing scalable backend systems in Python, integrating AI/ML models, optimizing data pipelines, and collaborating with teams to deliver high-performance solutions.

Lead the development and architecture of AI-driven features for mobile and web platforms, ensuring scalability and efficient model integration.

Lead the design and implementation of advanced AI-driven systems, integrating agentic AI with enterprise systems while ensuring security and scalability.

The Applied AI Engineer at DevRev will design, develop, and implement solutions that integrate the DevRev platform with customer technology stacks, utilizing AI and analytics to optimize customer engagement and workflows.

As a Senior Staff Software Engineer at Coinbase, you will lead AI infrastructure projects, write high-quality code in Python and Golang, mentor team members, and enhance system reliability and scalability.

Lead software engineering teams to develop and maintain scalable web applications, ensuring engineering excellence and promoting agile practices while mentoring team members.

Seeking a Backend Engineer to design and scale microservices and APIs for AI products, collaborating with ML engineers and optimizing performance.

Lead the architecture and development of Genesis AI for Expedite Commerce. Design and scale cloud-native, serverless architectures using AWS and integrate with Salesforce and enterprise ecosystems, while ensuring scalability and measurable outcomes.

Develop and maintain a real-time voice AI platform using Python. Focus on building systems for high concurrency, API design, and collaboration with ML teams.

Lead the technical evolution of a SaaS platform, driving design, development, and mentoring engineers while ensuring scalable and secure solutions.

As a Data & AI Engineer, you will design and implement AI solutions and ETL frameworks, manage data services, and develop software aligned with business goals.

The role involves designing and implementing Data & AI solutions, managing ETL processes, and building data services to improve customer retention.

Lead AI Developer and DevOps position focusing on AI model development, deployment, and infrastructure management using various ML and cloud technologies.

Lead a team of engineers in developing AI capabilities, managing projects and ensuring high-quality work while fostering a positive culture.

The Lead DevOps Engineer will support AI/ML systems deployment, CI/CD development, monitoring model performance, and collaborating with ML teams to optimize operations.

The Staff DevOps Engineer will work on deploying and maintaining AI/ML systems, supporting CI/CD pipelines, and collaborating with ML engineers in operations, focusing on MLOps best practices.

The Senior DevOps Engineer will build and maintain backend infrastructure for AI data pipelines, support model training and production, and develop monitoring systems.

Design and develop AI solutions for business challenges, manage data pipelines, and implement machine learning at scale, ensuring high-quality deliverables.

Lead a team to develop AI safety tools and platforms, ensuring responsible innovation and collaboration with customers. Mentor staff and guide projects in AI safety methodologies.