Top Machine Learning Jobs in Bangalore

Lead the design of distributed systems and ML infrastructure, drive innovation in AI and Big Data, mentor engineers, and ensure high-performance solutions.

The Senior Engineering Manager will lead innovation in AI-driven networking solutions, manage engineering teams, and oversee the full software development lifecycle, with a focus on Big Data and machine learning technologies.

The role involves leading software development for AI-driven network management, innovating ML solutions and collaborating on real-time ML platforms.

The Software Engineer will develop and deploy machine learning models, build recommender systems, and manage large-scale data pipelines to improve eBay's recommendation technology.

Design and maintain data infrastructure, develop CI/CD pipelines, support ETL processes, implement data governance, and mentor junior team members.

As a Software Engineer, you will lead the SDLC, design architecture, write clean code, and mentor teams while collaborating across domains.

You will design ML models to improve search and personalization, mentor junior engineers, and write maintainable code while collaborating with customers.

The Enterprise Architect is responsible for designing AI-powered enterprise solutions and leading teams in implementing and deploying AI/ML applications across various environments.

Lead the development of scalable backend infrastructure for LLM inferencing, API design, and agent communication in a multi-agent system.

Influence the architecture, design, and implementation of AI infrastructure and ML compiler. Develop scalable, high-performing AI/ML systems while collaborating with hardware/software teams and contributing to open frameworks.

The Senior SRE will design, build, and maintain services for data analytics and ML, ensuring reliability, scalability, and operational efficiency.

The Senior Machine Learning Engineer will develop and scale ML systems for cybersecurity, collaborating on building pipelines, ensuring reliability, and monitoring model performance.

The Staff Software Engineer will design and deploy ML algorithms, manage large data, and lead team efforts to enhance product offerings.

The Sr. Software Engineer will design machine learning algorithms, handle large unstructured data, and build data pipelines for e-commerce and consumer trends.

Design and maintain end-to-end MLOps pipelines, collaborate with Data Scientists, implement CI/CD pipelines, and ensure compliance and cost optimization of ML solutions on Azure.

The Director of Machine Learning Research will lead the ML research direction, oversee a team, and bridge research with product development, promoting innovative applications in AI and deep learning.

Design and develop scalable cloud platforms, advocate for agile methodologies, and support software engineers. Requires 7+ years experience in distributed systems.

Responsible for building automation and architecture around Data and AI/ML engineering on AWS and Kubernetes platform, ensuring best coding and CICD practices.

You will design and develop machine learning infrastructure and models, work with cross-functional teams, and build AI-driven applications, focusing on data scalability and performance.

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

As a Full Stack Machine Learning Engineer, you will develop AI-powered applications, integrate ML models, and collaborate with cross-functional teams to deliver impactful solutions.

Lead a team to develop scalable AI/ML solutions, shape AI strategy, oversee product integration, and ensure operational excellence.

Design and implement AI solutions, develop LLM-based applications, optimize retrieval pipelines, and maintain integrations while collaborating across teams.

The SDET-III will validate ML model performance and functionality, focusing on testing and backend automation for scaling applications.

As an ML Quality Engineer, you will ensure Cerebras software quality for ML workloads, drive best testing practices, build tools, and validate customer workflows.