Top Artificial Intelligence Jobs in Chennai

The Generative AI Architect will lead the design and development of generative AI systems, focusing on efficient architectures and integration with Azure and OpenAI technologies. Responsibilities include fine-tuning large language models, optimizing AI projects, and overseeing AI ethics and compliance. The role requires proficiency in Python and AI frameworks, and mentoring team members while staying current on AI advancements.

As a Data Scientist at Kyndryl, you will bridge business problems and innovative solutions using methodologies, statistics, and software engineering. You’ll analyze data for patterns, develop AI models, ensure data management, and promote data security and privacy. Your role includes deploying AI projects and collaborating with customers to drive data transformations, while continuously contributing to business understanding and change management.

The Platform AI Adoption Engineer will design, develop, and manage hardware and software infrastructures, ensuring quality standards and compliance. Responsibilities include product development support, mentoring, application prototyping, system troubleshooting, and project status reporting. Collaboration with development teams is essential to define application requirements and provide documentation and training.

The AI ML Engineering Manager will lead the team in developing Gen AI and ML solutions. Responsibilities include overseeing the design, development, and deployment of AI/ML models, managing a team, and collaborating effectively with cross-functional teams.

The Applied AI R&D Manager will lead a team of engineers and researchers in computer vision, managing AI product development from invention to deployment, collaborating with AI and HPC centers, driving innovation through hackathons, and contributing to KLA’s technical IP portfolio.

The role involves designing and developing machine learning software solutions for semiconductor metrology, collaborating with customers to define requirements, optimizing algorithms, extending prototypes into functional solutions, and maintaining software throughout its lifecycle. Documentation and technical support during testing phases are also key responsibilities.

The Staff Software Engineer will design and develop software solutions for Generative AI projects, implement comprehensive automated tests, and mentor team members. The role involves working with cloud platforms, microservices, and maintaining software applications while adhering to best practices and managing time zones for client interactions.

The Senior AI Engineer will develop, maintain, and deploy scalable AI pipelines and solutions, ensuring their efficiency and performance. Responsibilities include monitoring deployed systems, troubleshooting issues, optimizing processes, and implementing version control. The role involves collaboration with cross-functional teams to commercialize advanced AI products and solutions.

The role involves building enterprise-grade GenAI solutions for client deployment, focusing on ML project development, model deployment at scale, and engineering skills necessary for GenAI applications. Key responsibilities include data ingestion, leveraging LLMs, implementing RAG and prompt engineering, and monitoring ML model performance.

As a Cloud Backend Developer with AI at HPE, you will tackle engineering challenges related to distributed systems. Your role involves creating user-friendly software solutions from complex data technologies and building high-performance services for data streaming. Strong proficiency in Java, Python, and Kafka is essential, along with knowledge of database technologies and CI/CD practices.

As a Gen AI Developer, you will design, develop, and test software systems, focusing on cloud applications and micro-services architecture. Key responsibilities include coding, automated testing, software deployment, collaboration with product teams, and crafting large-scale distributed systems. You'll work with APIs, ML/AI algorithms, and both structured and unstructured data.

As a Software Quality Senior Engineer, you will lead testing efforts for sophisticated systems, work collaboratively within Agile teams, mentor junior testers, and ensure high software quality through automated testing and quality assurance practices.

The Senior Consultant in AI & Automation will design solutions for IT operations and business process automation, assess automation opportunities, and collaborate with various teams for effective solution consulting. Responsibilities include tool identification for AI solutions, proposal defense, customer interactions, and creating demo case studies. Extensive experience in IT operations and strong communication skills are essential.

The Senior Consultant in AI & Automation will design solutions for IT Operations and business process automation, assess automation opportunities, and contribute to solution consulting and proposal defense. Responsibilities include working with tools and platforms for AI & Automation solutions, collaborating with stakeholders, and engaging in technical discussions and demos.

The consultant will assess customer infrastructures and business processes, develop automation solutions using scripting languages, and integrate AI and ML capabilities. They will handle large datasets for model training, collaborate with IT teams, contribute to automation tools, and document all processes. Additionally, they will identify continuous improvement opportunities and provide training to team members.

The Senior Consultant will assess customer infrastructures to identify gaps, develop and implement automation solutions, and integrate AI/ML capabilities into workflows. Responsibilities include creating automation scripts, collaborating with IT teams, and delivering continuous service improvements.

The Senior Consultant will assess customer infrastructures to identify gaps, develop process improvement and automation solutions, maintain automation scripts, and integrate AI & ML capabilities. The role involves collaborating with IT teams, developing machine learning models, and contributing to internal tools and Microapps development, while ensuring continuous service improvements.